scattertext
0.0.2.4.4
Un outil pour trouver des termes distinctifs dans les corpus et les afficher dans un tracé de dispersion HTML interactif. Les points correspondant aux termes sont étiquetés sélectivement afin qu'ils ne chevauchent pas d'autres étiquettes ou points.
Citez comme: Jason S. Kessler. ScatterText: un outil basé sur un navigateur pour visualiser la difficulté des corpus. Démonstrations du système ACL. 2017.
Vous trouverez ci-dessous un exemple d'utilisation de ScatterText pour créer des termes visualisés utilisés dans les conventions politiques américaines 2012. Les 2 000 grammes UNI les plus associés aux partis sont affichés comme points dans le tracé de Scatter. Leurs axes X et Y sont les rangs denses de leur utilisation par des conférenciers républicains et démocrates respectivement.
import scattertext as st
df = st.SampleCorpora.ConventionData2012.get_data().assign(
parse=lambda df: df.text.apply(st.whitespace_nlp_with_sentences)
)
corpus = st.CorpusFromParsedDocuments(
df, category_col='party', parsed_col='parse'
).build().get_unigram_corpus().compact(st.AssociationCompactor(2000))
html = st.produce_scattertext_explorer(
corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
width_in_pixels=1000,
metadata=corpus.get_df()['speaker'],
transform=st.Scalers.dense_rank,
include_gradient=True,
left_gradient_term='More Republican',
middle_gradient_term='Metric: Dense Rank Difference',
right_gradient_term='More Democratic',
)
open('./demo_compact.html', 'w').write(html)
Le fichier HTML écrit ressemblerait à l'image ci-dessous. Cliquez dessus pour la visualisation interactive réelle.
Jason S. Kessler. ScatterText: un outil basé sur un navigateur pour visualiser la difficulté des corpus. Démonstrations du système ACL. 2017. Lien vers le papier: arxiv.org/abs/1703.00565
@article{kessler2017scattertext,
author = {Kessler, Jason S.},
title = {Scattertext: a Browser-Based Tool for Visualizing how Corpora Differ},
booktitle = {Proceedings of ACL-2017 System Demonstrations},
year = {2017},
address = {Vancouver, Canada},
publisher = {Association for Computational Linguistics},
}
Table des matières
Installation
Aperçu
Personnalisation de la visualisation et du traçage de la dispersion
Tutoriel
Comprendre la score F à l'échelle
Méthodes de notation des termes alternatifs
Le processus de position de position-sélection
Utilisations avancées
Exemples
Une note sur la disposition des graphiques
Quoi de neuf
Sources
Installez Python 3.11 ou plus et exécutez:
$ pip install scattertext
Si vous ne pouvez pas (ou ne souhaitez pas) installer Spacy, remplacez les lignes nlp = spacy.load('en') avec nlp = scattertext.WhitespaceNLP.whitespace_nlp . Remarque, ce n'est pas compatible avec word_similarity_explorer , et les capacités de détection des limites de tokenisation et de phrase seront des expressions régulières de faible performance. Voir demo_without_spacy.py pour un exemple.
Il est recommandé d'installer jieba , spacy , empath , astropy , flashtext , gensim et umap-learn afin de profiter pleinement du texte de dispersion.
ScatterText devrait principalement fonctionner avec Python 2.7, mais ce n'est peut-être pas le cas.
Les sorties HTML semblent les meilleures en chrome et en safari.
Le nom de ce projet est ScatterText. "ScatterText" est écrit comme un seul mot et doit être capitalisé. Lorsqu'elle est utilisée dans Python, le package scattertext doit être défini au nom st , c'est-à-dire import scattertext as st .
Il s'agit d'un outil destiné à visualiser ce que les mots et les phrases sont plus caractéristiques d'une catégorie que les autres.
Considérez l'exemple en haut de la page.
Regarder cela semble écrasant. En fait, c'est une visualisation relativement simple de l'utilisation des mots lors de la convention politique de 2012. Chaque point correspond à un mot ou une phrase mentionné par les républicains ou les démocrates lors de leurs conventions. Plus un point est proche du sommet de l'intrigue, plus il était fréquemment utilisé par les démocrates. Plus un point à droite plus loin, plus ce mot ou cette phrase a été utilisé par les républicains. Les mots fréquemment utilisés par les deux parties, comme "de" et "le" et même "mit" ont tendance à se produire dans le coin supérieur droit-main. Bien que les mots à très basse fréquence aient été cachés pour préserver les ressources informatiques, un mot qu'aucune des parties n'a utilisée, comme "girafe" ne serait dans le coin inférieur à gauche.
Les choses intéressantes se produisent près des coins supérieur gauche et inférieur à droite. Dans le coin supérieur gauche, des mots comme "Auto" (comme dans le renflouement automobile) et "millionnaires" sont fréquemment utilisés par les démocrates mais rarement ou jamais utilisés par les républicains. De même, les termes fréquemment utilisés par les républicains et rarement par les démocrates occupent le coin inférieur droit. Il s'agit notamment du "grand gouvernement" et des "Jeux olympiques", se référant aux Jeux olympiques de Salt Lake City auxquels le gouverneur Romney était impliqué.
Les termes sont colorés par leur association. Ceux qui sont plus associés aux démocrates sont bleus et ceux plus associés aux républicains rouges.
Les termes qui sont les plus caractéristiques des deux ensembles de documents sont affichés à l'extrême droite de la visualisation.
L'inspiration pour cette visualisation est venue de DataclySm (Rudder, 2014).
ScatterText est conçu pour vous aider à créer ces graphiques et à étiqueter efficacement les points.
La documentation (y compris cette lecture) est un travail en cours. Veuillez consulter le tutoriel ci-dessous ainsi que le tutoriel Pydata 2017.
Pousser autour du code et des tests devrait vous donner une bonne idée de la façon dont les choses fonctionnent.
La bibliothèque couvre quelques formules de terme nouvelles et efficaces, y compris les sages F à l'échelle .
Nouveau dans ScatterText 0.1.0, on peut utiliser un DataFrame pour les positions de terme / métadonnées et d'autres données spécifiques à des termes. Nous pouvons également l'utiliser pour déterminer les informations spécifiques au terme qui sont affichées après un terme cliqué.
Notez qu'il est possible de désactiver l'utilisation des catégories de documents dans ScatterText, comme nous le verrons dans cet exemple.
Cet exemple couvre la dispersion des termes de traçage par rapport à la fréquence des mots et identifiant les termes qui sont les plus dispersés et les moins dispersés compte tenu de leurs fréquences. En utilisant la mesure de dispersion de Rosengren (GRIES 2021), les termes ont tendance à augmenter de leurs scores de dispersion à mesure qu'ils deviennent plus fréquents. Nous verrons comment nous pouvons à la fois tracer cet effet et prendre en compte l'effet de la fréquence.
Ceci, ainsi qu'un certain nombre d'autres mesures de dispersion présentées dans GRIES (2021), sont disponibles et documentés dans la classe Dispersion , que nous utiliserons plus tard dans la section.
Commençons par créer un corpus de congrès, mais nous utiliserons l'usine Corpus CorpusWithoutCategoriesFromParsedDocuments pour nous assurer qu'aucune catégories n'est incluse dans le corpus. Si nous essayons de trouver des catégories de documents, nous verrons que tous les documents ont la catégorie «_».
import scattertext as st
df = st . SampleCorpora . ConventionData2012 . get_data (). assign (
parse = lambda df : df . text . apply ( st . whitespace_nlp_with_sentences ))
corpus = st . CorpusWithoutCategoriesFromParsedDocuments (
df , parsed_col = 'parse'
). build (). get_unigram_corpus (). remove_infrequent_words ( minimum_term_count = 6 )
corpus . get_categories ()
# Returns ['_']Ensuite, nous allons créer une dataframe pour tous les termes que nous allons tracer. Nous allons simplement commencer par créer un dataframe où nous capturerons la fréquence de chaque terme et diverses mesures de dispersion. Ceux-ci seront affichés une fois qu'un terme est activé dans le tracé.
dispersion = st . Dispersion ( corpus )
dispersion_df = dispersion . get_df ()
dispersion_df . head ( 3 )Qui revient
Frequency Range SD VC Juilland's D Rosengren's S DP DP norm KL-divergence Dissemination
thank 363 134 3.108113 1.618274 0.707416 0.694898 0.391548 0.391560 0.748808 0.972954
you 1630 177 12.383708 1.435902 0.888596 0.898805 0.233627 0.233635 0.263337 0.963905
so 549 155 3.523380 1.212967 0.774299 0.822244 0.283151 0.283160 0.411750 0.986423```
These are discussed in detail in [Gries 2021](http://www.stgries.info/research/ToApp_STG_Dispersion_PHCL.pdf).
Dissementation is presented in Altmann et al. (2011).
We'll use Rosengren's S to find the dispersion of each term. It's which a metric designed for corpus parts
(convention speeches in our case) of varying length. Where n is the number of documents in the corpus, s_i is the
percentage of tokens in the corpus found in document i, v_i is term count in document i, and f is the total number
of tokens in the corpus of type term type.
Rosengren's
S: [^2}{f})](https://render.githubusercontent.com/render/math?math=frac{Sum_{i=1}^{n}sqrt{s_i%20cdot%20v_i})
^2}{f})
In order to start plotting, we'll need to add coordinates for each term to the data frame.
To use the `dataframe_scattertext` function, you need, at a minimum a dataframe with 'X' and 'Y' columns.
The `Xpos` and `Ypos` columns indicate the positions of the original `X` and `Y` values on the scatterplot, and
need to be between 0 and 1. Functions in `st.Scalers` perform this scaling. Absent `Xpos` or `Ypos`,
`st.Scalers.scale` would be used.
Here is a sample of values:
* `st.Scalers.scale(vec)` Rescales the vector to where the minimum value is 0 and the maximum is 1.
* `st.Scalers.log_scale(vec)` Rescales the lgo of the vector
* `st.Scalers.dense_ranke(vec)` Rescales the dense rank of the vector
* `st.Scalers.scale_center_zero_abs(vec)` Rescales a vector with both positive and negative values such that the 0 value
in the original vector is plotted at 0.5, negative values are projected from [-argmax(abs(vec)), 0] to [0, 0.5] and
positive values projected from [0, argmax(abs(vec))] to [0.5, 1].
```python
dispersion_df = dispersion_df.assign(
X=lambda df: df.Frequency,
Xpos=lambda df: st.Scalers.log_scale(df.X),
Y=lambda df: df["Rosengren's S"],
Ypos=lambda df: st.Scalers.scale(df.Y),
)
Notez que la colonne Ypos ici n'est pas nécessaire car Y serait automatiquement à l'échelle.
Enfin, comme nous ne distinguons pas entre les catégories, nous pouvons définir ignore_categories=True .
Nous pouvons maintenant tracer ce graphique à l'aide de la fonction dataframe_scattertext :
html = st . dataframe_scattertext (
corpus ,
plot_df = dispersion_df ,
metadata = corpus . get_df ()[ 'speaker' ] + ' (' + corpus . get_df ()[ 'party' ]. str . upper () + ')' ,
ignore_categories = True ,
x_label = 'Log Frequency' ,
y_label = "Rosengren's S" ,
y_axis_labels = [ 'Less Dispersion' , 'Medium' , 'More Dispersion' ],
)Qui donne (cliquez pour une version interactive):
Notez que nous pouvons voir diverses statistiques de dispersion sous le nom d'un terme, en plus des statistiques d'utilisation standard. Pour personnaliser les statistiques affichées, définissez le paramètre term_description_column=[...] avec une liste de noms de colonne à afficher.
Un problème dans ce tableau de dispersion, qui a tendance à être commun aux mesures de dispersion en général, est que la dispersion et la fréquence ont tendance à avoir une corrélation élevée, mais avec une courbe complexe non linéaire. Selon la métrique, cette courbe de corrélation pourrait être la puissance, le linéaire, le sigmoïdal ou généralement autre chose.
Afin de prendre en compte cette corrélation, nous pouvons prédire la dispersion de la fréquence à l'aide d'un régresseur non paramétrique, et voir quels termes ont les résidus les plus élevés et les plus bas par rapport à leurs dispersions attendues en fonction de leurs fréquences.
Dans ce cas, nous utiliserons un régresseur KNN avec 10 voisins pour prédire les Rosengren à partir des fréquences à terme ( dispersion_df.X et .Y respectivement), et calculer le résidu.
Nous allons les points résiduels à couleur, avec une couleur neutre pour les résidus autour de 0 et d'autres couleurs pour des valeurs positives et négatives. Nous allons ajouter une colonne dans le cadre de données pour les couleurs ponctuelles et l'appeler ColorsCore. Il est peuplé de valeurs comprises entre 0 et 1, avec 0,5 comme couleur neturale sur l'échelle de couleur d3 interpolateWarm . Nous utilisons st.Scalers.scale_center_zero_abs , discuté ci-dessus, pour effectuer cette transformation.
from sklearn . neighbors import KNeighborsRegressor
dispersion_df = dispersion_df . assign (
Expected = lambda df : KNeighborsRegressor ( n_neighbors = 10 ). fit (
df . X . values . reshape ( - 1 , 1 ), df . Y
). predict ( df . X . values . reshape ( - 1 , 1 )),
Residual = lambda df : df . Y - df . Expected ,
ColorScore = lambda df : st . Scalers . scale_center_zero_abs ( df . Residual )
) Maintenant, nous sommes prêts à tracer notre tableau de dispersion coloré. Nous attribuons le nom de colonne ColorsCore au paramètre color_score_column dans dataframe_scattertext .
De plus, nous aimerions remplir les listes de deux terme à gauche avec des termes qui ont des valeurs résiduelles élevées et faibles, indiquant des termes qui ont le plus de dispersion par rapport à leur niveau prévu en fréquence et le plus bas. Nous pouvons le faire par le paramètre left_list_column . Nous pouvons spécifier les noms de liste de termes supérieurs et inférieurs à l'aide du paramètre header_names . Enfin, nous pouvons épousturer l'intrigue en ajoutant une couleur de fond attrayante.
html = st . dataframe_scattertext (
corpus ,
plot_df = dispersion_df ,
metadata = corpus . get_df ()[ 'speaker' ] + ' (' + corpus . get_df ()[ 'party' ]. str . upper () + ')' ,
ignore_categories = True ,
x_label = 'Log Frequency' ,
y_label = "Rosengren's S" ,
y_axis_labels = [ 'Less Dispersion' , 'Medium' , 'More Dispersion' ],
color_score_column = 'ColorScore' ,
header_names = { 'upper' : 'Lower than Expected' , 'lower' : 'More than Expected' },
left_list_column = 'Residual' ,
background_color = '#e5e5e3'
)Qui donne (cliquez pour une version interactive):
Bien que vous devriez apprendre Python entièrement à utiliser ScatterText, j'ai mis certaines des fonctionnalités de base dans un outil de ligne de commande. L'outil est installé lorsque vous suivez la procédure disposée ci-dessus.
Exécutez $ scattertext --help à partir de la ligne de commande pour voir les informations d'utilisation complètes. Voici un exemple rapide de la façon d'utiliser Vanilla ScatterText sur un fichier CSV. Le fichier doit avoir au moins deux colonnes, une contenant le texte à analyser et une autre contenant la catégorie. Dans l'exemple CSV ci-dessous, les colonnes sont respectivement du texte et du parti.
L'exemple ci-dessous traite le fichier CSV et la visualisation HTML résultante dans CLI_DEMO.html.
Remarque, le paramètre --minimum_term_frequency=8 omit les termes qui se produisent moins de 8 fois, et --regex_parser indique qu'un analyseur d'expression régulière simple doit être utilisé à la place de la spacie. Le drapeau --one_use_per_doc indique que la fréquence du terme doit être calculée en ne comptant que plus d'une occurrence d'un terme dans un document.
Si vous souhaitez analyser le texte non anglophone, vous pouvez utiliser l'argument --spacy_language_model pour configurer le modèle de langage spacy que l'outil utilisera. La valeur par défaut est «en» et vous pouvez voir les autres disponibles sur https://spacy.io/docs/api/language-models.
$ curl -s https://cdn.rawgit.com/JasonKessler/scattertext/master/scattertext/data/political_data.csv | head -2
party,speaker,text
democrat,BARACK OBAMA, " Thank you. Thank you. Thank you. Thank you so much.Thank you.Thank you so much. Thank you. Thank you very much, everybody. Thank you.
$
$ scattertext --datafile=https://cdn.rawgit.com/JasonKessler/scattertext/master/scattertext/data/political_data.csv
> --text_column=text --category_column=party --metadata_column=speaker --positive_category=democrat
> --category_display_name=Democratic --not_category_display_name=Republican --minimum_term_frequency=8
> --one_use_per_doc --regex_parser --outputfile=cli_demo.htmlLe code suivant crée un fichier HTML autonome qui analyse les mots utilisés par les démocrates et les républicains dans les conventions du parti 2012, et produit des associations de termes notables.
Tout d'abord, importez ScatterText et Spacy.
>>> import scattertext as st
>>> import spacy
>>> from pprint import pprint
Ensuite, assemblez les données que vous souhaitez analyser dans un cadre de données Pandas. Il devrait avoir au moins deux colonnes, le texte que vous souhaitez analyser et la catégorie que vous souhaitez étudier. Ici, la colonne text contient des discours de convention tandis que la colonne party contient le parti de l'orateur. Nous allons éventuellement utiliser la colonne speaker pour étiqueter les extraits de visualisation.
>>> convention_df = st.SampleCorpora.ConventionData2012.get_data()
>>> convention_df.iloc[0]
party democrat
speaker BARACK OBAMA
text Thank you. Thank you. Thank you. Thank you so ...
Name: 0, dtype: object
Transformez le cadre de données en un corpus de dispersion pour commencer à les analyser. Pour rechercher des différences dans les parties, définissez le paramètre category_col sur 'party' et utilisez les discours, présents dans la colonne text , comme les textes à analyser en définissant le paramètre COL text . Enfin, transmettez un modèle Spacy à l'argument nlp et appelez build() pour construire le corpus.
# Turn it into a Scattertext Corpus
>>> nlp = spacy.load('en')
>>> corpus = st.CorpusFromPandas(convention_df,
... category_col='party',
... text_col='text',
... nlp=nlp).build()
Voyons les termes caractéristiques du corpus et les termes qui sont les plus démocrates et les républicains associés. Voir les diapositives 52 à 59 du contenu non structuré de tournant des noyaux d'idées parler pour plus de détails sur ces approches.
Voici les termes qui différencient le corpus d'un corpus anglais général.
>>> print(list(corpus.get_scaled_f_scores_vs_background().index[:10]))
['obama',
'romney',
'barack',
'mitt',
'obamacare',
'biden',
'romneys',
'hardworking',
'bailouts',
'autoworkers']
Voici les termes qui sont les plus associés aux démocrates:
>>> term_freq_df = corpus.get_term_freq_df()
>>> term_freq_df['Democratic Score'] = corpus.get_scaled_f_scores('democrat')
>>> pprint(list(term_freq_df.sort_values(by='Democratic Score', ascending=False).index[:10]))
['auto',
'america forward',
'auto industry',
'insurance companies',
'pell',
'last week',
'pell grants',
"women 's",
'platform',
'millionaires']
Et républicains:
>>> term_freq_df['Republican Score'] = corpus.get_scaled_f_scores('republican')
>>> pprint(list(term_freq_df.sort_values(by='Republican Score', ascending=False).index[:10]))
['big government',
"n't build",
'mitt was',
'the constitution',
'he wanted',
'hands that',
'of mitt',
'16 trillion',
'turned around',
'in florida']
Maintenant, écrivons le tracé de dispersion un fichier HTML autonome. Nous ferons la catégorie de l'axe y "démocrate" et nommerons la catégorie "démocrate" avec une capitale "D" à des fins de présentation. Nous nommerons l'autre catégorie "républicain" avec une capitale "R". Tous les documents dans le corpus sans la catégorie "démocrate" seront considérés comme républicains. Nous définissons la largeur de la visualisation dans les pixels et étiquette chaque extrait avec le haut-parleur en utilisant le paramètre metadata . Enfin, nous écrivons la visualisation dans un fichier HTML.
>>> html = st.produce_scattertext_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... width_in_pixels=1000,
... metadata=convention_df['speaker'])
>>> open("Convention-Visualization.html", 'wb').write(html.encode('utf-8'))
Vous trouverez ci-dessous à quoi ressemble la page Web. Cliquez dessus et attendez quelques minutes pour la version interactive.
ScatterText peut également être utilisé pour visualiser l'association de catégorie d'une variété de différents types de phrases. Le mot "phrase" désigne toute collocation unique ou multi-mots.
Pytextrank, créé par Paco Nathan, est une implémentation d'une version modifiée de l'algorithme Textrank (Mihalcea et Tarau 2004). Il implique un algorithme de centralité graphique pour extraire une liste notée des phrases les plus importantes d'un document. Ici, des entités nommées reconnues par Spacy. À partir de la version 2.2 de Spacy, celles-ci proviennent d'un système NER formé sur les ontonotes 5.
Veuillez installer pytextrank $ pip3 install pytextrank avant de continuer avec ce tutoriel.
Pour utiliser, créez un corpus comme d'habitude, mais assurez-vous d'utiliser Spacy pour analyser chaque document par opposition à un tokenizer whitespace_nlp intégré. Notez que l'ajout de pytextrank au pipeline Spacy n'est pas nécessaire, car il sera exécuté séparément par l'objet PyTextRankPhrases . Nous réduirons le nombre de phrases affichées dans le graphique à 2000 en utilisant l' AssociationCompactor . Les phrases générées seront traitées comme des caractéristiques non textuelles car leurs scores de document ne correspondront pas aux comptages de mots.
import pytextrank, spacy
import scattertext as st
nlp = spacy.load('en')
nlp.add_pipe("textrank", last=True)
convention_df = st.SampleCorpora.ConventionData2012.get_data().assign(
parse=lambda df: df.text.apply(nlp),
party=lambda df: df.party.apply({'democrat': 'Democratic', 'republican': 'Republican'}.get)
)
corpus = st.CorpusFromParsedDocuments(
convention_df,
category_col='party',
parsed_col='parse',
feats_from_spacy_doc=st.PyTextRankPhrases()
).build(
).compact(
AssociationCompactor(2000, use_non_text_features=True)
)
Notez que les termes présents dans le corpus sont nommés entités et, par opposition aux dénombrements de fréquences, leurs scores sont les scores de concentesse qui leur sont attribués par l'algorithme Textrank. L'exécution corpus.get_metadata_freq_df('') reviendra, pour chaque catégorie, les sommes des scores textrank. Les rangs denses de ces scores seront utilisés pour construire le tracé de dispersion.
term_category_scores = corpus.get_metadata_freq_df('')
print(term_category_scores)
'''
Democratic Republican
term
our future 1.113434 0.699103
your country 0.314057 0.000000
their home 0.385925 0.000000
our government 0.185483 0.462122
our workers 0.199704 0.210989
her family 0.540887 0.405552
our time 0.510930 0.410058
...
'''
Avant de construire l'intrigue, certaines variables d'assistance car les scores de Textrank agrégés ne sont pas particulièrement interprétables, nous afficherons le rang par catégorie de chaque score dans le champ metadata_description . Ceux-ci seront affichés après un terme cliqué.
term_ranks = pd.DataFrame(
np.argsort(np.argsort(-term_category_scores, axis=0), axis=0) + 1,
columns=term_category_scores.columns,
index=term_category_scores.index)
metadata_descriptions = {
term: '<br/>' + '<br/>'.join(
'<b>%s</b> TextRank score rank: %s/%s' % (cat, term_ranks.loc[term, cat], corpus.get_num_metadata())
for cat in corpus.get_categories())
for term in corpus.get_metadata()
}
Nous pouvons construire des scores de terme de deux manières. L'un est une différence de rang dense standard, une partition qui est utilisée dans la plupart des parcelles contrastives à deux catégories ici, qui nous donneront les phrases les plus associées à la catégorie. Un autre consiste à utiliser le score maximal spécifique à la catégorie, cela nous donnera les phrases les plus importantes de chaque catégorie, quelle que soit la proéminence dans l'autre catégorie. Nous allons adopter les deux approches dans ce tutoriel, calculons le deuxième type de score, la proéminence spécifique à la catégorie ci-dessous.
category_specific_prominence = term_category_scores.apply(
lambda r: r.Democratic if r.Democratic > r.Republican else -r.Republican,
axis=1
)
Maintenant, nous sommes prêts à sortir ce graphique. Notez que nous utilisons une transformée dense_rank , qui place des phrases festonnées identiques les unes aux autres. Nous utilisons category_specific_prominence comme scores, et définissons sort_by_dist comme False pour s'assurer que les phrases affichées sur le côté droit du graphique sont classées par les scores et non à la distance par rapport aux coins supérieurs à gauche ou en bas à droite. Étant donné que les phrases correspondantes sont traitées comme des fonctionnalités non texte, nous les codant pour les modèles de sujets à phrase unique et définissons le topic_model_preview_size sur 0 pour indiquer que la liste des modèles de sujets ne doit pas être affichée. Enfin, nous définissons assurer que les documents complets sont affichés. Remarque Les documents seront affichés par ordre de score spécifique à la phrase.
html = produce_scattertext_explorer(
corpus,
category='Democratic',
not_category_name='Republican',
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
width_in_pixels=1000,
transform=dense_rank,
metadata=corpus.get_df()['speaker'],
scores=category_specific_prominence,
sort_by_dist=False,
use_non_text_features=True,
topic_model_term_lists={term: [term] for term in corpus.get_metadata()},
topic_model_preview_size=0,
metadata_descriptions=metadata_descriptions,
use_full_doc=True
)
Les termes les plus associés dans chaque catégorie ont un sens, au moins sur une analyse post hoc. En faisant référence au (alors) gouverneur Romney, les démocrates ont utilisé son nom de famille "Romney" dans leurs mentions les plus centrales de lui, tandis que les républicains ont utilisé le "mit" plus familier et humanisant. En ce qui concerne le président Obama, l'expression "Obama" ne s'est pas présentée en tant que trimestre de premier plan, le mais le prénom "Barack" était l'une des phrases les plus centrales des discours démocratiques, reflétant "Mitt".
Alternativement, nous pouvons dense la différence de classement dans les scores aux points de phrase couleur et déterminer les phrases supérieures à afficher sur le côté droit du graphique. Au lieu de définir scores en tant que scores de proéminence spécifiques à la catégorie, nous définissons term_scorer=RankDifference() pour injecter un moyen de déterminer les scores de terme dans le processus de création de tracé de dispersion.
html = produce_scattertext_explorer(
corpus,
category='Democratic',
not_category_name='Republican',
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
width_in_pixels=1000,
transform=dense_rank,
use_non_text_features=True,
metadata=corpus.get_df()['speaker'],
term_scorer=RankDifference(),
sort_by_dist=False,
topic_model_term_lists={term: [term] for term in corpus.get_metadata()},
topic_model_preview_size=0,
metadata_descriptions=metadata_descriptions,
use_full_doc=True
)
La phrasémachine d'Abehandler (Handler et al. 2016) utilise des expressions régulières sur des séquences de balises de disposition pour identifier les phrases nominales. Cela a l'avantage sur l'utilisation de la suppression de NP de Spacy en ce qu'il a tendance à isoler les grandes phases du nom significatives qui sont exemptes d'appositives.
Par opposition à Pytextrank, nous allons simplement utiliser des dénombrements de ces phrases, les traitant comme n'importe quel autre terme.
import spacy
from scattertext import SampleCorpora, PhraseMachinePhrases, dense_rank, RankDifference, AssociationCompactor, produce_scattertext_explorer
from scattertext.CorpusFromPandas import CorpusFromPandas
corpus = (CorpusFromPandas(SampleCorpora.ConventionData2012.get_data(),
category_col='party',
text_col='text',
feats_from_spacy_doc=PhraseMachinePhrases(),
nlp=spacy.load('en', parser=False))
.build().compact(AssociationCompactor(4000)))
html = produce_scattertext_explorer(corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
transform=dense_rank,
metadata=corpus.get_df()['speaker'],
term_scorer=RankDifference(),
width_in_pixels=1000)
Dans ScatterText, diverses mesures, y compris les associations à terme, sont souvent montrées de deux manières. Le premier et le plus important est la position dans le graphique. Le second est la couleur d'un point ou d'un texte. Dans ScatterText 0.2.21, un moyen de visualiser la sémantique de ces scores est introduit: le gradient comme clé.
Le gradient, par défaut, suit le paramètre d3_color_scale de produce_scattertext_explorer qui est d3.interpolateRdYlBu par défaut.
Les paramètres supplémentaires suivants pour produce_scattertext_explorer (et des fonctions similaires) permettent les gradients de manipulation.
include_gradient: bool ( False par défaut) est un drapeau qui déclenche l'apparence d'un dégradé.left_gradient_term: Optional[str] indique le texte écrit du côté lointain gauche du gradient. Il est écrit en gradient_text_color et est category_name par défaut.right_gradient_term: Optional[str] indique le texte écrit du côté lointain gauche du gradient. Il est écrit en gradient_text_color et n'est not_category_name par défaut.middle_gradient_term: Optional[str] indique le texte écrit au milieu du gradient. C'est la couleur opposée de la couleur du gradient central et est vide par défaut.gradient_text_color: Optional[str] indique la couleur fixe du texte écrit sur le gradient. Si aucun, il est par défaut de la couleur opposée du dégradé.left_text_color: Optional[str] remplace gradient_text_color pour le terme de gradient gauchemiddle_text_color: Optional[str] remplace gradient_text_color pour le terme de gradient moyenright_text_color: Optional[str] remplace gradient_text_color pour le terme de dégradé droitgradient_colors: Optional[List[str]] des couleurs hexadécimales, y compris '#', (par exemple, ['#0000ff', '#980067', '#cc3300', '#32cd00'] ) qui décrivent le dégradé. S'il est donné, ceux-ci remplacent d3_color_scale . Un exemple simple est le suivant. Les couleurs de terme sont définies comme une cartographie entre un nom de terme et une couleur #RRGGBB dans le cadre du paramètre term_color , et le gradient de couleur est défini dans gradient_colors . Le
import matplotlib . pyplot as plt
import matplotlib as mpl
df = st . SampleCorpora . ConventionData2012 . get_data (). assign (
parse = lambda df : df . text . apply ( st . whitespace_nlp_with_sentences )
)
corpus = st . CorpusFromParsedDocuments (
df , category_col = 'party' , parsed_col = 'parse'
). build (). get_unigram_corpus (). compact ( st . AssociationCompactor ( 2000 ))
html = st . produce_scattertext_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
minimum_term_frequency = 0 ,
pmi_threshold_coefficient = 0 ,
width_in_pixels = 1000 ,
metadata = corpus . get_df ()[ 'speaker' ],
transform = st . Scalers . dense_rank ,
include_gradient = True ,
left_gradient_term = "More Democratic" ,
right_gradient_term = "More Republican" ,
middle_gradient_term = 'Metric: Dense Rank Difference' ,
gradient_text_color = "white" ,
term_colors = dict ( zip (
corpus . get_terms (),
[
mpl . colors . to_hex ( x ) for x in plt . get_cmap ( 'brg' )(
st . Scalers . scale_center_zero_abs (
st . RankDifferenceScorer ( corpus ). set_categories ( 'democrat' ). get_scores ()). values
)
]
)),
gradient_colors = [ mpl . colors . to_hex ( x ) for x in plt . get_cmap ( 'brg' )( np . arange ( 1. , 0. , - 0.01 ))],
) Afin de visualiser les sujets et catégories Empath (Fast et al., 2016) au lieu des termes, nous devons créer un Corpus de sujets et de catégories extraits plutôt que d'unigrammes et de bigrams. Pour ce faire, utilisez l'extracteur de fonctionnalités FeatsOnlyFromEmpath . Voir le code source pour des exemples de la façon de créer le vôtre.
Lors de la création de la visualisation, passez l' use_non_text_features=True argument dans produce_scattertext_explorer . Cela lui demandera d'utiliser les sujets et catégories Empath étiquetés au lieu de rechercher des termes. Étant donné que les documents renvoyés lorsqu'un sujet ou une étiquette de catégorie est cliqué sera dans l'ordre de la force d'association de catégorie de niveau de document, la définition use_full_doc=True est logique, sauf si vous avez d'énormes documents. Sinon, les 300 premiers caractères seront affichés.
(Nouveau en 0,0,26). Assurez-vous d'inclure topic_model_term_lists=feat_builder.get_top_model_term_lists() dans produce_scattertext_explorer pour s'assurer qu'il en gras des passages des extraits qui correspondent au modèle de sujet.
>>> feat_builder = st.FeatsFromOnlyEmpath()
>>> empath_corpus = st.CorpusFromParsedDocuments(convention_df,
... category_col='party',
... feats_from_spacy_doc=feat_builder,
... parsed_col='text').build()
>>> html = st.produce_scattertext_explorer(empath_corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... width_in_pixels=1000,
... metadata=convention_df['speaker'],
... use_non_text_features=True,
... use_full_doc=True,
... topic_model_term_lists=feat_builder.get_top_model_term_lists())
>>> open("Convention-Visualization-Empath.html", 'wb').write(html.encode('utf-8'))
C ScatterText comprend également un constructeur de fonctionnalités pour explorer la relation entre les catégories General Inquirer Tag et les catégories de documents. Nous utiliserons une approche légèrement différente, en examinant la relation entre les catégories de balises GI et les partis politiques en utilisant les scores Z du log-odds-ratio avec des priors de Dirichlet non informatifs (Monroe 2008). Nous utiliserons la variation de tracé produce_frequency_explorer pour visualiser cette relation, en définissant l'axe x comme le nombre de fois qu'un mot dans la catégorie de balise se produit, et l'axe y comme score Z.
Pour plus d'informations sur le General Inquirer, veuillez consulter la page d'accueil du General Inquirer.
Nous utiliserons le même ensemble de données qu'auparavant, sauf que nous utiliserons le constructeur de fonctionnalités FeatsFromGeneralInquirer .
>>> general_inquirer_feature_builder = st.FeatsFromGeneralInquirer()
>>> corpus = st.CorpusFromPandas(convention_df,
... category_col='party',
... text_col='text',
... nlp=st.whitespace_nlp_with_sentences,
... feats_from_spacy_doc=general_inquirer_feature_builder).build()
Ensuite, nous appellerons produce_frequency_explorer de la même manière que nous avons appelé produce_scattertext_explorer dans la section précédente. Il y a cependant quelques différences. Premièrement, nous spécifions le buteur de termes LogOddsRatioUninformativeDirichletPrior , qui marque les relations entre les catégories. Le grey_threshold indique que les points de points entre [-1,96, 1,96] (c'est-à-dire p> 0,05) doivent être gris colorés. L'argument metadata_descriptions=general_inquirer_feature_builder.get_definitions() indique qu'un dictionnaire mappant le nom de la balise à une définition de chaîne est passé. Lorsqu'une balise est cliquée, la définition du dictionnaire sera affichée sous le tracé, comme indiqué dans l'image suivant l'extrait.
>>> html = st.produce_frequency_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... metadata=convention_df['speaker'],
... use_non_text_features=True,
... use_full_doc=True,
... term_scorer=st.LogOddsRatioUninformativeDirichletPrior(),
... grey_threshold=1.96,
... width_in_pixels=1000,
... topic_model_term_lists=general_inquirer_feature_builder.get_top_model_term_lists(),
... metadata_descriptions=general_inquirer_feature_builder.get_definitions())
Voici le graphique résultant.
La [théorie des fondations morales] propose six constructions psychologiques comme des éléments constitutifs de la pensée morale, comme décrit dans Graham et al. (2013). Ces fondements sont, comme décrit sur [moralfoundations.org]: soins / préjudice, équité / tricherie, loyauté / trahison, autorité / subversion, sainteté / dégradation et liberté / oppression. Veuillez consulter le site pour une discussion plus approfondie de ces fondations.
Frimer et al. (2019) ont créé le Dictionary 2.0 des fondations morales, ou un lexique des termes qui invoque une fondation morale en tant que vertu (favorable à la fondation) ou vice (en opposition à la fondation).
Ce dictionnaire peut être utilisé de la même manière que le General Inquirer. Dans cet exemple, nous pouvons tracer les scores D de Cohen des comptes de mots de fondation par rapport aux fréquences que les mots impliquant ces fondations ont été invoqués.
Nous pouvons d'abord charger le corpus comme d'habitude, et utiliser st.FeatsFromMoralFoundationsDictionary() pour extraire les fonctionnalités.
import scattertext as st
convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
moral_foundations_feats = st . FeatsFromMoralFoundationsDictionary ()
corpus = st . CorpusFromPandas ( convention_df ,
category_col = 'party' ,
text_col = 'text' ,
nlp = st . whitespace_nlp_with_sentences ,
feats_from_spacy_doc = moral_foundations_feats ). build ()Ensuite, utilisons le buteur du terme D de Cohen pour analyser le corpus et décrivons un ensemble de scores d'association D de Cohen.
cohens_d_scorer = st . CohensD ( corpus ). use_metadata ()
term_scorer = cohens_d_scorer . set_categories ( 'democrat' , [ 'republican' ]). term_scorer . get_score_df ()Qui donne le cadre de données suivant:
| cohens_d | cohens_d_se | cohens_d_z | cohens_d_p | HEDGES_G | HEDGES_G_SE | HEDGES_G_Z | HEDGES_G_P | M1 | M2 | comte1 | comte2 | docs1 | docs2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| care.virtue | 0,662891 | 0.149425 | 4.43629 | 4.57621E-06 | 0,660257 | 0.159049 | 4.15129 | 1.65302E-05 | 0.195049 | 0.12164 | 760 | 379 | 115 | 54 |
| soins. | 0,24435 | 0.146025 | 1.67335 | 0,0471292 | 0,243379 | 0.152654 | 1.59432 | 0,0554325 | 0,0580005 | 0,0428358 | 244 | 121 | 80 | 41 |
| saut. | 0.176794 | 0.145767 | 1.21286 | 0,112592 | 0.176092 | 0.152164 | 1.15725 | 0.123586 | 0,0502469 | 0,0403369 | 225 | 107 | 71 | 39 |
| Équité. | 0,0707162 | 0.145528 | 0.485928 | 0,313509 | 0,0704352 | 0.151711 | 0,464273 | 0,321226 | 0,00718627 | 0,00573227 | 32 | 14 | 21 | 10 |
| autorité. | -0.0187793 | 0.145486 | -0.12908 | 0,551353 | -0.0187047 | 0.15163 | -0.123357 | 0,549088 | 0,358192 | 0,361191 | 1281 | 788 | 122 | 66 |
| Autorité. | -0.0354164 | 0.145494 | -0,243422 | 0,596161 | -0.0352757 | 0.151646 | -0,232619 | 0,591971 | 0,00353465 | 0,00390602 | 20 | 14 | 14 | 10 |
| sainteté. | -0,512145 | 0.147848 | -3.46399 | 0.999734 | -0,51011 | 0.156098 | -3.26788 | 0.999458 | 0,0587987 | 0.101677 | 265 | 309 | 74 | 48 |
| vice sainte | -0.108011 | 0.145589 | -0,74189 | 0,770923 | -0.107582 | 0.151826 | -0,708585 | 0,760709 | 0,00845048 | 0,0109339 | 35 | 28 | 23 | 20 |
| fidélité. | -0,413696 | 0.147031 | -2.81367 | 0.997551 | -0,412052 | 0,154558 | -2,666 | 0.996162 | 0,259296 | 0.309776 | 1056 | 717 | 119 | 66 |
| fidélité.vice | -0.0854683 | 0.145549 | -0,587213 | 0,72147 | -0.0851287 | 0.151751 | -0,560978 | 0,712594 | 0,00124518 | 0,00197022 | 5 | 5 | 5 | 4 |
Ce cadre de données nous donne les scores D de Cohen (et leurs erreurs standard et z-scores), Hedge's
Notez que le D de Cohen est la différence de M1 et M2 divisé par leur écart-type regroupé.
Maintenant, complotons les scores D des fondations par rapport à leurs fréquences.
html = st . produce_frequency_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
metadata = convention_df [ 'speaker' ],
use_non_text_features = True ,
use_full_doc = True ,
term_scorer = st . CohensD ( corpus ). use_metadata (),
grey_threshold = 0 ,
width_in_pixels = 1000 ,
topic_model_term_lists = moral_foundations_feats . get_top_model_term_lists (),
metadata_descriptions = moral_foundations_feats . get_definitions ()
)Souvent, les termes de l'intérêt sont ceux qui sont caractéristiques du corpus dans son ensemble. Ce sont des termes qui se produisent fréquemment dans tous les ensembles de documents étudiés, mais relativement peu fréquents par rapport aux fréquences générales des termes.
Nous pouvons produire un tracé avec un score caractéristique sur l'axe X et les scores d'association de classe sur l'axe Y en utilisant la fonction produce_characteristic_explorer .
La caractéristique du corpus est la différence en termes denses se classe entre les mots de tous les documents de l'étude et une liste générale de fréquences de langue anglaise. Voir ce discours sur les scores de l'association de classe à terme pour une explication plus approfondie.
import scattertext as st
corpus = ( st . CorpusFromPandas ( st . SampleCorpora . ConventionData2012 . get_data (),
category_col = 'party' ,
text_col = 'text' ,
nlp = st . whitespace_nlp_with_sentences )
. build ()
. get_unigram_corpus ()
. compact ( st . ClassPercentageCompactor ( term_count = 2 ,
term_ranker = st . OncePerDocFrequencyRanker )))
html = st . produce_characteristic_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
metadata = corpus . get_df ()[ 'speaker' ]
)
open ( 'demo_characteristic_chart.html' , 'wb' ). write ( html . encode ( 'utf-8' )) En plus des mots, des phases et des sujets, nous pouvons faire correspondre chaque point correspondant à un document. Créons d'abord un objet Corpus pour l'ensemble de données des conventions de 2012. Cette explication suit demo_pca_documents.py
import pandas as pd
from sklearn . feature_extraction . text import TfidfTransformer
import scattertext as st
from scipy . sparse . linalg import svds
convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
convention_df [ 'parse' ] = convention_df [ 'text' ]. apply ( st . whitespace_nlp_with_sentences )
corpus = ( st . CorpusFromParsedDocuments ( convention_df ,
category_col = 'party' ,
parsed_col = 'parse' )
. build ()
. get_stoplisted_unigram_corpus ()) Ensuite, ajoutons les noms de documents sous forme de méta-données dans l'objet Corpus. La fonction add_doc_names_as_metadata prend un tableau de noms de documents et remplit les méta-données d'un nouveau corpus avec ces noms. Si deux documents ont le même nom, il ajoute un nombre (en commençant par 1) au nom.
corpus = corpus . add_doc_names_as_metadata ( corpus . get_df ()[ 'speaker' ])Ensuite, nous trouvons des scores TF.idf pour la matrice de termes à terme du corpus, exécutons un SVD clairsemé et les ajoutons à un cadre de données de projection, ce qui rend les axes X et Y les deux premières valeurs singulières, et les indexant sur les méta-données du corpus, qui correspond aux noms de documents.
embeddings = TfidfTransformer (). fit_transform ( corpus . get_term_doc_mat ())
u , s , vt = svds ( embeddings , k = 3 , maxiter = 20000 , which = 'LM' )
projection = pd . DataFrame ({ 'term' : corpus . get_metadata (), 'x' : u . T [ 0 ], 'y' : u . T [ 1 ]}). set_index ( 'term' ) Enfin, définir les scores 1 pour les démocrates et 0 pour les républicains, rendant les documents républicains comme des points rouges et des documents démocratiques comme bleu. Pour en savoir plus sur la fonction produce_pca_explorer , voir à l'aide de SVD pour visualiser tout type d'incorporation de mots.
category = 'democrat'
scores = ( corpus . get_category_ids () == corpus . get_categories (). index ( category )). astype ( int )
html = st . produce_pca_explorer ( corpus ,
category = category ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
metadata = convention_df [ 'speaker' ],
width_in_pixels = 1000 ,
show_axes = False ,
use_non_text_features = True ,
use_full_doc = True ,
projection = projection ,
scores = scores ,
show_top_terms = False )Cliquez pour une version interactive
Le D de Cohen est une métrique populaire utilisée pour mesurer la taille de l'effet. Les définitions du D et de la Hedge de Cohen
> >> convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
> >> corpus = ( st . CorpusFromPandas ( convention_df ,
... category_col = 'party' ,
... text_col = 'text' ,
... nlp = st . whitespace_nlp_with_sentences )
.... build ()
.... get_unigram_corpus ())Nous pouvons créer un objet de buteur à terme pour examiner les tailles d'effet et autres mesures.
>> > term_scorer = st . CohensD ( corpus ). set_categories ( 'democrat' , [ 'republican' ])
>> > term_scorer . get_score_df (). sort_values ( by = 'cohens_d' , ascending = False ). head ()
cohens_d
cohens_d_se
cohens_d_z
cohens_d_p
hedges_g
hedges_g_se
hedges_g_z
hedges_g_p
m1
m2
obama
1.187378
0.024588
48.290444
0.000000e+00
1.187322
0.018419
64.461363
0.0
0.007778
0.002795
class 0.855859 0.020848 41.052045 0.000000e+00 0.855818 0.017227 49.677688 0.0 0.002222 0.000375
middle
0.826895
0.020553
40.232746
0.000000e+00
0.826857
0.017138
48.245626
0.0
0.002316
0.000400
president
0.820825
0.020492
40.056541
0.000000e+00
0.820786
0.017120
47.942661
0.0
0.010231
0.005369
barack
0.730624
0.019616
37.245725
6.213052e-304
0.730589
0.016862
43.327800
0.0
0.002547
0.000725 Notre calcul du D de Cohen n'est pas directement basé sur le nombre de termes. Nous divisons plutôt les termes de chaque document par le nombre total de termes dans le document avant de calculer les statistiques. m1 et m2 sont respectivement les parties moyennes de mots dans les discours prononcés par les démocrates et les républicains qui étaient le terme en question. La taille de l'effet ( cohens_d ) est la différence entre ces moyennes divisées par l'écart-type regroupé. cohens_d_se est l'erreur standard de la statistique, tandis que cohens_d_z et cohens_d_p sont les scores Z et P indiquant la signification statistique de l'effet. Des colonnes correspondantes sont présentes pour les Hedge
> >> st . produce_frequency_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
term_scorer = st . CohensD ( corpus ),
metadata = convention_df [ 'speaker' ],
grey_threshold = 0
)Cliquez pour une version interactive.
Cliff's Delta (Cliff 1993) utilise une approche non paramétrique de la taille de l'effet informatique. Dans notre cadre, le pourcentage de fréquence du terme de chaque document dans l'ensemble de mise au point est comparé à celui de l'ensemble d'arrière-plan. Pour chaque paire de documents, un score de 1 est donné si le pourcentage de fréquence du document de mise au point est supérieur à l'arrière-plan, 0 s'il est identique et -1 si différent. Notez que cela suppose que les longueurs de document sont réparties de manière similaire dans les corpus de mise au point et d'arrière-plan.
Voir [https://real-statistics.com/non-parametric-tests/mann-whitney-test/cliffs-delta/] pour les formules utilisées dans CliffsDelta .
Vous trouverez ci-dessous un exemple d'utilisation de CliffsDelta pour trouver et tracer les scores de terme:
nlp = spacy . blank ( 'en' )
nlp . add_pipe ( 'sentencizer' )
convention_df = st . SampleCorpora . ConventionData2012 . get_data (). assign (
party = lambda df : df . party . apply (
lambda x : { 'democrat' : 'Dem' , 'republican' : 'Rep' }[ x ]),
SpacyParse = lambda df : df . text . progress_apply ( nlp )
)
corpus = st . CorpusFromParsedDocuments ( convention_df , category_col = 'party' , parsed_col = 'SpacyParse' ). build (
). remove_terms_used_in_less_than_num_docs ( 10 )
st . CliffsDelta ( corpus ). set_categories ( 'Dem' ). get_score_df (). sort_values ( by = 'Dem' , ascending = False ). iloc [: 10 ]| terme | Métrique | Stddev | CI faible à 5,0% | IC high-0,0% | TermCount1 | TermCount2 | Doccount1 | Doccount2 |
|---|---|---|---|---|---|---|---|---|
| Obama | 0,597191 | 0,0266606 | -1,35507 | -1.03477 | 537 | 165 | 113 | 40 |
| Président Obama | 0,565903 | 0,0314348 | -2,37978 | -1,74131 | 351 | 78 | 100 | 30 |
| président | 0,426337 | 0,0293418 | 1.22784 | 0.909226 | 740 | 301 | 113 | 53 |
| milieu | 0.417591 | 0,0267365 | 1.10791 | 0,840932 | 164 | 27 | 68 | 12 |
| classe | 0,415373 | 0,0280622 | 1.09032 | 0,815649 | 161 | 25 | 69 | 14 |
| barack | 0.406997 | 0,0281692 | 1.00765 | 0,750963 | 202 | 46 | 76 | 16 |
| Barack Obama | 0,402562 | 0,027512 | 0,965359 | 0,723403 | 164 | 45 | 76 | 16 |
| C'est | 0,384085 | 0,0227344 | 0.809747 | 0,634705 | 236 | 91 | 89 | 31 |
| Obama. | 0,356245 | 0,0237453 | 0,664688 | 0,509631 | 70 | 5 | 49 | 4 |
| pour | 0,35526 | 0,0364138 | 0,70142 | 0,46487 | 1020 | 542 | 119 | 62 |
Nous pouvons afficher élégamment les scores Delta de la falaise à l'aide dataframe_scattertext , et décrire le schéma de coloriage ponctuel à l'aide du paramètre include_gradient=True . Nous définissons les paramètres left_gradient_term , middle_gradient_term et right_gradient_term sur des chaînes qui apparaîtront dans leurs valeurs de corsaire.
plot_df = st . CliffsDelta (
corpus
). set_categories (
category_name = 'Dem'
). get_score_df (). rename ( columns = { 'Metric' : 'CliffsDelta' }). assign (
Frequency = lambda df : df . TermCount1 + df . TermCount1 ,
X = lambda df : df . Frequency ,
Y = lambda df : df . CliffsDelta ,
Xpos = lambda df : st . Scalers . dense_rank ( df . X ),
Ypos = lambda df : st . Scalers . scale_center_zero_abs ( df . Y ),
ColorScore = lambda df : df . Ypos ,
)
html = st . dataframe_scattertext (
corpus ,
plot_df = plot_df ,
category = 'Dem' ,
category_name = 'Dem' ,
not_category_name = 'Rep' ,
width_in_pixels = 1000 ,
ignore_categories = False ,
metadata = lambda corpus : corpus . get_df ()[ 'speaker' ],
color_score_column = 'ColorScore' ,
left_list_column = 'ColorScore' ,
show_characteristic = False ,
y_label = "Cliff's Delta" ,
x_label = 'Frequency Ranks' ,
y_axis_labels = [ f'More Rep: delta= { plot_df . CliffsDelta . max ():.3f } ' ,
'' ,
f'More Dem: delta= { - plot_df . CliffsDelta . max ():.3f } ' ],
tooltip_columns = [ 'Frequency' , 'CliffsDelta' ],
term_description_columns = [ 'CliffsDelta' , 'Stddev' , 'Low-95.0% CI' , 'High-95.0% CI' ],
header_names = { 'upper' : 'Top Dem' , 'lower' : 'Top Reps' },
horizontal_line_y_position = 0 ,
include_gradient = True ,
left_gradient_term = 'More Republican' ,
right_gradient_term = 'More Democratic' ,
middle_gradient_term = "Metric: Cliff's Delta" ,
) La séparation bi-normale (BNS) (Forman, 2008) a été ajoutée dans la version 0.1.8. Une variation de (BNS) est utilisée où
corpus = ( st . CorpusFromPandas ( convention_df ,
category_col = 'party' ,
text_col = 'text' ,
nlp = st . whitespace_nlp_with_sentences )
. build ()
. get_unigram_corpus ()
. remove_infrequent_words ( 3 , term_ranker = st . OncePerDocFrequencyRanker ))
term_scorer = ( st . BNSScorer ( corpus ). set_categories ( 'democrat' ))
print ( term_scorer . get_score_df (). sort_values ( by = 'democrat BNS' ))
html = st . produce_frequency_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
scores = term_scorer . get_score_df ()[ 'democrat BNS' ]. reindex ( corpus . get_terms ()). values ,
metadata = lambda c : c . get_df ()[ 'speaker' ],
minimum_term_frequency = 0 ,
grey_threshold = 0 ,
y_label = f'Bi-normal Separation (alpha= { term_scorer . prior_counts } )'
)BNS a marqué des termes en utilisant un alpha trouvé algorithmique. ! [Bns] (https://raw.githubusercontent.com/jasonkessler/jasonkessler.github.io/master/d emo_bi_normal_separation.png)
Nous pouvons former un classificateur pour produire un score de prédiction pour chaque document. Souvent, les classificateurs ou les régresseurs utilisent des fonctionnalités qui prennent en compte les fonctionnalités au-delà de celles représentées par la dispersion, qu'elles soient n-gram, le sujet, extra-linguistique, neuronal, etc.
Nous pouvons utiliser ScatterText pour visualiser les corrélations entre les unigrammes (ou vraiment n'importe quelle représentation de fonctionnalité) et les scores de document produits par un modèle.
Dans l'exemple suivant, nous formons un SVM linéaire en utilisant des fonctionnalités d'unigram et de bi-grammes sur l'ensemble de données de convention, et utilisons le modèle pour faire une prédiction sur chaque document, et enfin en utilisant Pearson
from sklearn . svm import LinearSVC
import scattertext as st
df = st . SampleCorpora . ConventionData2012 . get_data (). assign (
parse = lambda df : df . text . apply ( st . whitespace_nlp_with_sentences )
)
corpus = st . CorpusFromParsedDocuments (
df , category_col = 'party' , parsed_col = 'parse'
). build ()
X = corpus . get_term_doc_mat ()
y = corpus . get_category_ids ()
clf = LinearSVC ()
clf . fit ( X = X , y = y == corpus . get_categories (). index ( 'democrat' ))
doc_scores = clf . decision_function ( X = X )
compactcorpus = corpus . get_unigram_corpus (). compact ( st . AssociationCompactor ( 2000 ))
plot_df = st . Correlations (). set_correlation_type (
'pearsonr'
). get_correlation_df (
corpus = compactcorpus ,
document_scores = doc_scores
). reindex ( compactcorpus . get_terms ()). assign (
X = lambda df : df . Frequency ,
Y = lambda df : df [ 'r' ],
Xpos = lambda df : st . Scalers . dense_rank ( df . X ),
Ypos = lambda df : st . Scalers . scale_center_zero_abs ( df . Y ),
SuppressDisplay = False ,
ColorScore = lambda df : df . Ypos ,
)
html = st . dataframe_scattertext (
compactcorpus ,
plot_df = plot_df ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
width_in_pixels = 1000 ,
metadata = lambda c : c . get_df ()[ 'speaker' ],
unified_context = False ,
ignore_categories = False ,
color_score_column = 'ColorScore' ,
left_list_column = 'ColorScore' ,
y_label = "Pearson r (correlation to SVM document score)" ,
x_label = 'Frequency Ranks' ,
header_names = { 'upper' : 'Top Democratic' ,
'lower' : 'Top Republican' },
) ScatterText s'appuie sur un ensemble de fréquences de mots anglaises du domaine général lors du calcul des caractéristiques de l'Unigram
scores. Lorsque vous utilisez l'exécution de ScatterText sur des données non anglophones ou dans un domaine spécifique, la qualité des scores se dégradera.
Assurez-vous que vous êtes sur ScatterText 0.1.6 ou plus.
Pour y remédier, on peut ajouter un ensemble personnalisé de scores d'arrière-plan à un objet de type corpus, en utilisant la fonction Corpus.set_background_corpus . La fonction prend un objet pd.Series , indexé sur les termes avec des valeurs de nombre numérique.
Par défaut, [! Comprendre la échelle-f-score] (score F à l'échelle) est utilisé pour classer à quel point les termes caractéristiques sont.
L'exemple ci-dessous illustre l'utilisation des fréquences de mot d'arrière-plan polonaises.
Tout d'abord, nous produisons un objet de série mappant des mots polonais à leurs fréquences en utilisant une liste à partir du https://github.com/oprogramador/most-common-words-by-language Repo.
polish_word_frequencies = pd . read_csv (
'https://raw.githubusercontent.com/hermitdave/FrequencyWords/master/content/2016/pl/pl_50k.txt' ,
sep = ' ' ,
names = [ 'Word' , 'Frequency' ]
). set_index ( 'Word' )[ 'Frequency' ]Notez la composition de la série
>> > polish_word_frequencies
Word
nie
5875385
to
4388099
się
3507076
w
2723767
na
2309765
Name : Frequency , dtype : int64 Ensuite, nous construisons un DataFrame, reviews_df , composé d'un document qui apparaît (à un haut-parleur non polonais) pour être des critiques d'hôtel positives et négatives à partir des https://klejbenchmark.com/tasks/ corpus (Kocoń, et al. 2019). Note this data is under a CC BY-NC-SA 4.0 license. These are labeled as "__label__meta_plus_m" and "__label__meta_minus_m". We will use Scattertext to compare those reviews and determine
nlp = spacy . blank ( 'pl' )
nlp . add_pipe ( 'sentencizer' )
with ZipFile ( io . BytesIO ( urlopen (
'https://klejbenchmark.com/static/data/klej_polemo2.0-in.zip'
). read ())) as zf :
review_df = pd . read_csv ( zf . open ( 'train.tsv' ), sep = ' t ' )[
lambda df : df . target . isin ([ '__label__meta_plus_m' , '__label__meta_minus_m' ])
]. assign (
Parse = lambda df : df . sentence . apply ( nlp )
) Next, we wish to create a ParsedCorpus object from review_df . In preparation, we first assemble a list of Polish stopwords from the stopwords repository. We also create the not_a_word regular expression to filter out terms which do not contain a letter.
polish_stopwords = {
stopword for stopword in
urlopen (
'https://raw.githubusercontent.com/bieli/stopwords/master/polish.stopwords.txt'
). read (). decode ( 'utf-8' ). split ( ' n ' )
if stopword . strip ()
}
not_a_word = re . compile ( r'^W+$' ) With these present, we can build a corpus from review_df with the category being the binary "target" column. We reduce the term space to unigrams and then run the filter_out which takes a function to determine if a term should be removed from the corpus. The function identifies terms which are in the Polish stoplist or do not contain a letter. Finally, terms occurring less than 20 times in the corpus are removed.
We set the background frequency Series we created early as the background corpus.
corpus = st . CorpusFromParsedDocuments (
review_df ,
category_col = 'target' ,
parsed_col = 'Parse'
). build (
). get_unigram_corpus (
). filter_out (
lambda term : term in polish_stopwords or not_a_word . match ( term ) is not None
). remove_infrequent_words (
minimum_term_count = 20
). set_background_corpus (
polish_word_frequencies
)Note that a minimum word count of 20 was chosen to ensure that only around 2,000 terms would be displayed
>> > corpus . get_num_terms ()
2023 Running get_term_and_background_counts shows us total term counts in the corpus compare to background frequency counts. We limit this to terms which only occur in the corpus.
>> > corpus . get_term_and_background_counts ()[
...
lambda df : df . corpus > 0
...]. sort_values ( by = 'corpus' , ascending = False )
background
corpus
m
341583838.0
4819.0
hotelu
33108.0
1812.0
hotel
297974790.0
1651.0
doktor
154840.0
1534.0
polecam
0.0
1438.0
.........
szoku
0.0
21.0
badaniem
0.0
21.0
balkonu
0.0
21.0
stopnia
0.0
21.0
wobec
0.0
21.0Interesting, the term "polecam" appears very frequently in the corpus, but does not appear at all in the background corpus, making it highly characteristic. Judging from Google Translate, it appears to mean something related to "recommend".
We are now ready to display the plot.
html = st . produce_scattertext_explorer (
corpus ,
category = '__label__meta_plus_m' ,
category_name = 'Plus-M' ,
not_category_name = 'Minus-M' ,
minimum_term_frequency = 1 ,
width_in_pixels = 1000 ,
transform = st . Scalers . dense_rank
) We can change the formula which is used to produce the Characteristic scores using the characteristic_scorer parameter to produce_scattertext_explorer .
It takes a instance of a descendant of the CharacteristicScorer class. See DenseRankCharacteristicness.py for an example of how to make your own.
Example of plotting with a modified characteristic scorer,
html = st . produce_scattertext_explorer (
corpus ,
category = '__label__meta_plus_m' ,
category_name = 'Plus-M' ,
not_category_name = 'Minus-M' ,
minimum_term_frequency = 1 ,
transform = st . Scalers . dense_rank ,
characteristic_scorer = st . DenseRankCharacteristicness (),
term_ranker = st . termranking . AbsoluteFrequencyRanker ,
term_scorer = st . ScaledFScorePresets ( beta = 1 , one_to_neg_one = True )
). encode ( 'utf-8' ))
print ( 'open ' + fn )Note that numbers show up as more characteristic using the Dense Rank Difference. It may be they occur unusually frequently in this corpus, or perhaps the background word frequencies under counted mumbers.
Word productivity is one strategy for plotting word-based charts describing an uncategorized corpus.
Productivity is defined in Schumann (2016) (Jason: check this) as the entropy of ngrams which contain a term. For the entropy computation, the probability of an n-gram wrt the term whose productivity is being calculated is the frequency of the n-gram divided by the term's frequency.
Since productivity highly correlates with frequency, the recommended metric to plot is the dense rank difference between frequency and productivity.
The snippet below plots words in the convention corpus based on their log frequency and their productivity.
The function st.whole_corpus_productivity_scores returns a DataFrame giving each word's productivity. For example, in the convention corpus,
Productivity scores should be calculated on a Corpus -like object which contains a complete set of unigrams and at least bigrams. This corpus should not be compacted before the productivity score calculation.
The terms with lower productivity have more limited usage (eg, "thank" for "thank you", "united" for "united steates") while the terms with higher productivity occurr in a wider varity of contexts ("getting", "actually", "political", etc.).
import spacy
import scattertext as st
corpus_no_cat = st . CorpusWithoutCategoriesFromParsedDocuments (
st . SampleCorpora . ConventionData2012 . get_data (). assign (
Parse = lambda df : [ x for x in spacy . load ( 'en_core_web_sm' ). pipe ( df . text )]),
parsed_col = 'Parse'
). build ()
compact_corpus_no_cat = corpus_no_cat . get_stoplisted_unigram_corpus (). remove_infrequent_words ( 9 )
plot_df = st . whole_corpus_productivity_scores ( corpus_no_cat ). assign (
RankDelta = lambda df : st . RankDifference (). get_scores (
a = df . Productivity ,
b = df . Frequency
)
). reindex (
compact_corpus_no_cat . get_terms ()
). dropna (). assign (
X = lambda df : df . Frequency ,
Xpos = lambda df : st . Scalers . log_scale ( df . Frequency ),
Y = lambda df : df . RankDelta ,
Ypos = lambda df : st . Scalers . scale ( df . RankDelta ),
)
html = st . dataframe_scattertext (
compact_corpus_no_cat . whitelist_terms ( plot_df . index ),
plot_df = plot_df ,
metadata = lambda df : df . get_df ()[ 'speaker' ],
ignore_categories = True ,
x_label = 'Rank Frequency' ,
y_label = "Productivity" ,
left_list_column = 'Ypos' ,
color_score_column = 'Ypos' ,
y_axis_labels = [ 'Least Productive' , 'Average Productivity' , 'Most Productive' ],
header_names = { 'upper' : 'Most Productive' , 'lower' : 'Least Productive' , 'right' : 'Characteristic' },
horizontal_line_y_position = 0
)Let's now turn our attention to a novel term scoring metric, Scaled F-Score. We'll examine this on a unigram version of the Rotten Tomatoes corpus (Pang et al. 2002). It contains excerpts of positive and negative movie reviews.
Please see Scaled F Score Explanation for a notebook version of this analysis.
from scipy . stats import hmean
term_freq_df = corpus . get_unigram_corpus (). get_term_freq_df ()[[ 'Positive freq' , 'Negative freq' ]]
term_freq_df = term_freq_df [ term_freq_df . sum ( axis = 1 ) > 0 ]
term_freq_df [ 'pos_precision' ] = ( term_freq_df [ 'Positive freq' ] * 1. /
( term_freq_df [ 'Positive freq' ] + term_freq_df [ 'Negative freq' ]))
term_freq_df [ 'pos_freq_pct' ] = ( term_freq_df [ 'Positive freq' ] * 1.
/ term_freq_df [ 'Positive freq' ]. sum ())
term_freq_df [ 'pos_hmean' ] = ( term_freq_df
. apply ( lambda x : ( hmean ([ x [ 'pos_precision' ], x [ 'pos_freq_pct' ]])
if x [ 'pos_precision' ] > 0 and x [ 'pos_freq_pct' ] > 0
else 0 ), axis = 1 ))
term_freq_df . sort_values ( by = 'pos_hmean' , ascending = False ). iloc [: 10 ]If we plot term frequency on the x-axis and the percentage of a term's occurrences which are in positive documents (ie, its precision) on the y-axis, we can see that low-frequency terms have a much higher variation in the precision. Given these terms have low frequencies, the harmonic means are low. Thus, the only terms which have a high harmonic mean are extremely frequent words which tend to all have near average precisions.
freq = term_freq_df . pos_freq_pct . values
prec = term_freq_df . pos_precision . values
html = st . produce_scattertext_explorer (
corpus . remove_terms ( set ( corpus . get_terms ()) - set ( term_freq_df . index )),
category = 'Positive' ,
not_category_name = 'Negative' ,
not_categories = [ 'Negative' ],
x_label = 'Portion of words used in positive reviews' ,
original_x = freq ,
x_coords = ( freq - freq . min ()) / freq . max (),
x_axis_values = [ int ( freq . min () * 1000 ) / 1000. ,
int ( freq . max () * 1000 ) / 1000. ],
y_label = 'Portion of documents containing word that are positive' ,
original_y = prec ,
y_coords = ( prec - prec . min ()) / prec . max (),
y_axis_values = [ int ( prec . min () * 1000 ) / 1000. ,
int (( prec . max () / 2. ) * 1000 ) / 1000. ,
int ( prec . max () * 1000 ) / 1000. ],
scores = term_freq_df . pos_hmean . values ,
sort_by_dist = False ,
show_characteristic = False
)
file_name = 'not_normed_freq_prec.html'
open ( file_name , 'wb' ). write ( html . encode ( 'utf-8' ))
IFrame ( src = file_name , width = 1300 , height = 700 ) from scipy . stats import norm
def normcdf ( x ):
return norm . cdf ( x , x . mean (), x . std ())
term_freq_df [ 'pos_precision_normcdf' ] = normcdf ( term_freq_df . pos_precision )
term_freq_df [ 'pos_freq_pct_normcdf' ] = normcdf ( term_freq_df . pos_freq_pct . values )
term_freq_df [ 'pos_scaled_f_score' ] = hmean (
[ term_freq_df [ 'pos_precision_normcdf' ], term_freq_df [ 'pos_freq_pct_normcdf' ]])
term_freq_df . sort_values ( by = 'pos_scaled_f_score' , ascending = False ). iloc [: 10 ] freq = term_freq_df . pos_freq_pct_normcdf . values
prec = term_freq_df . pos_precision_normcdf . values
html = st . produce_scattertext_explorer (
corpus . remove_terms ( set ( corpus . get_terms ()) - set ( term_freq_df . index )),
category = 'Positive' ,
not_category_name = 'Negative' ,
not_categories = [ 'Negative' ],
x_label = 'Portion of words used in positive reviews (norm-cdf)' ,
original_x = freq ,
x_coords = ( freq - freq . min ()) / freq . max (),
x_axis_values = [ int ( freq . min () * 1000 ) / 1000. ,
int ( freq . max () * 1000 ) / 1000. ],
y_label = 'documents containing word that are positive (norm-cdf)' ,
original_y = prec ,
y_coords = ( prec - prec . min ()) / prec . max (),
y_axis_values = [ int ( prec . min () * 1000 ) / 1000. ,
int (( prec . max () / 2. ) * 1000 ) / 1000. ,
int ( prec . max () * 1000 ) / 1000. ],
scores = term_freq_df . pos_scaled_f_score . values ,
sort_by_dist = False ,
show_characteristic = False
) term_freq_df [ 'neg_precision_normcdf' ] = normcdf (( term_freq_df [ 'Negative freq' ] * 1. /
( term_freq_df [ 'Negative freq' ] + term_freq_df [ 'Positive freq' ])))
term_freq_df [ 'neg_freq_pct_normcdf' ] = normcdf (( term_freq_df [ 'Negative freq' ] * 1.
/ term_freq_df [ 'Negative freq' ]. sum ()))
term_freq_df [ 'neg_scaled_f_score' ] = hmean (
[ term_freq_df [ 'neg_precision_normcdf' ], term_freq_df [ 'neg_freq_pct_normcdf' ]])
term_freq_df [ 'scaled_f_score' ] = 0
term_freq_df . loc [ term_freq_df [ 'pos_scaled_f_score' ] > term_freq_df [ 'neg_scaled_f_score' ],
'scaled_f_score' ] = term_freq_df [ 'pos_scaled_f_score' ]
term_freq_df . loc [ term_freq_df [ 'pos_scaled_f_score' ] < term_freq_df [ 'neg_scaled_f_score' ],
'scaled_f_score' ] = 1 - term_freq_df [ 'neg_scaled_f_score' ]
term_freq_df [ 'scaled_f_score' ] = 2 * ( term_freq_df [ 'scaled_f_score' ] - 0.5 )
term_freq_df . sort_values ( by = 'scaled_f_score' , ascending = True ). iloc [: 10 ] is_pos = term_freq_df . pos_scaled_f_score > term_freq_df . neg_scaled_f_score
freq = term_freq_df . pos_freq_pct_normcdf * is_pos - term_freq_df . neg_freq_pct_normcdf * ~ is_pos
prec = term_freq_df . pos_precision_normcdf * is_pos - term_freq_df . neg_precision_normcdf * ~ is_pos
def scale ( ar ):
return ( ar - ar . min ()) / ( ar . max () - ar . min ())
def close_gap ( ar ):
ar [ ar > 0 ] -= ar [ ar > 0 ]. min ()
ar [ ar < 0 ] -= ar [ ar < 0 ]. max ()
return ar
html = st . produce_scattertext_explorer (
corpus . remove_terms ( set ( corpus . get_terms ()) - set ( term_freq_df . index )),
category = 'Positive' ,
not_category_name = 'Negative' ,
not_categories = [ 'Negative' ],
x_label = 'Frequency' ,
original_x = freq ,
x_coords = scale ( close_gap ( freq )),
x_axis_labels = [ 'Frequent in Neg' ,
'Not Frequent' ,
'Frequent in Pos' ],
y_label = 'Precision' ,
original_y = prec ,
y_coords = scale ( close_gap ( prec )),
y_axis_labels = [ 'Neg Precise' ,
'Imprecise' ,
'Pos Precise' ],
scores = ( term_freq_df . scaled_f_score . values + 1 ) / 2 ,
sort_by_dist = False ,
show_characteristic = False
) We can use st.ScaledFScorePresets as a term scorer to display terms' Scaled F-Score on the y-axis and term frequencies on the x-axis.
html = st . produce_frequency_explorer (
corpus . remove_terms ( set ( corpus . get_terms ()) - set ( term_freq_df . index )),
category = 'Positive' ,
not_category_name = 'Negative' ,
not_categories = [ 'Negative' ],
term_scorer = st . ScaledFScorePresets ( beta = 1 , one_to_neg_one = True ),
metadata = rdf [ 'movie_name' ],
grey_threshold = 0
)Scaled F-Score is not the only scoring method included in Scattertext. Please click on one of the links below to view a notebook which describes how other class association scores work and can be visualized through Scattertext.
New in 0.0.2.73 is the delta JS-Divergence scorer DeltaJSDivergence scorer (Gallagher et al. 2020), and its corresponding compactor (JSDCompactor.) See demo_deltajsd.py for an example usage.
New in 0.0.2.72
Scattertext was originally set up to visualize corpora objects, which are connected sets of documents and terms to visualize. The "compaction" process allows users to eliminate terms which may not be associated with a category using a variety of feature selection methods. The issue with this is that the terms eliminated during the selection process are not taken into account when scaling term positions.
This issue can be mitigated by using the position-select-plot process, where term positions are pre-determined before the selection process is made.
Let's first use the 2012 conventions corpus, update the category names, and create a unigram corpus.
import scattertext as st
import numpy as np
df = st . SampleCorpora . ConventionData2012 . get_data (). assign (
parse = lambda df : df . text . apply ( st . whitespace_nlp_with_sentences )
). assign ( party = lambda df : df [ 'party' ]. apply ({ 'democrat' : 'Democratic' , 'republican' : 'Republican' }. get ))
corpus = st . CorpusFromParsedDocuments (
df , category_col = 'party' , parsed_col = 'parse'
). build (). get_unigram_corpus ()
category_name = 'Democratic'
not_category_name = 'Republican'Next, let's create a dataframe consisting of the original counts and their log-scale positions.
def get_log_scale_df ( corpus , y_category , x_category ):
term_coord_df = corpus . get_term_freq_df ( '' )
# Log scale term counts (with a smoothing constant) as the initial coordinates
coord_columns = []
for category in [ y_category , x_category ]:
col_name = category + '_coord'
term_coord_df [ col_name ] = np . log ( term_coord_df [ category ] + 1e-6 ) / np . log ( 2 )
coord_columns . append ( col_name )
# Scale these coordinates to between 0 and 1
min_offset = term_coord_df [ coord_columns ]. min ( axis = 0 ). min ()
for coord_column in coord_columns :
term_coord_df [ coord_column ] -= min_offset
max_offset = term_coord_df [ coord_columns ]. max ( axis = 0 ). max ()
for coord_column in coord_columns :
term_coord_df [ coord_column ] /= max_offset
return term_coord_df
# Get term coordinates from original corpus
term_coordinates = get_log_scale_df ( corpus , category_name , not_category_name )
print ( term_coordinates ) Here is a preview of the term_coordinates dataframe. The Democrat and Republican columns contain the term counts, while the _coord columns contain their logged coordinates. Visualizing 7,973 terms is difficult (but possible) for people running Scattertext on most computers.
Democratic Republican Democratic_coord Republican_coord
term
thank 158 205 0.860166 0.872032
you 836 794 0.936078 0.933729
so 337 212 0.894681 0.873562
much 84 76 0.831380 0.826820
very 62 75 0.817543 0.826216
... ... ... ... ...
precinct 0 2 0.000000 0.661076
godspeed 0 1 0.000000 0.629493
beauty 0 1 0.000000 0.629493
bumper 0 1 0.000000 0.629493
sticker 0 1 0.000000 0.629493
[7973 rows x 4 columns]
We can visualize this full data set by running the following code block. We'll create a custom Javascript function to populate the tooltip with the original term counts, and create a Scattertext Explorer where the x and y coordinates and original values are specified from the data frame. Additionally, we can use show_diagonal=True to draw a dashed diagonal line across the plot area.
You can click the chart below to see the interactive version. Note that it will take a while to load.
# The tooltip JS function. Note that d is is the term data object, and ox and oy are the original x- and y-
# axis counts.
get_tooltip_content = ('(function(d) {return d.term + "<br/>' + not_category_name + ' Count: " ' +
'+ d.ox +"<br/>' + category_name + ' Count: " + d.oy})')
html_orig = st.produce_scattertext_explorer(
corpus,
category=category_name,
not_category_name=not_category_name,
minimum_term_frequency=0,
pmi_threshold_coefficient=0,
width_in_pixels=1000,
metadata=corpus.get_df()['speaker'],
show_diagonal=True,
original_y=term_coordinates[category_name],
original_x=term_coordinates[not_category_name],
x_coords=term_coordinates[category_name + '_coord'],
y_coords=term_coordinates[not_category_name + '_coord'],
max_overlapping=3,
use_global_scale=True,
get_tooltip_content=get_tooltip_content,
)
Next, we can visualize the compacted version of the corpus. The compaction, using ClassPercentageCompactor , selects terms which frequently in each category. The term_count parameter, set to 2, is used to determine the percentage threshold for terms to keep in a particular category. This is done using by calculating the percentile of terms (types) in each category which appear more than two times. We find the smallest percentile, and only include terms which occur above that percentile in a given category.
Note that this compaction leaves only 2,828 terms. This number is much easier for Scattertext to display in a browser.
# Select terms which appear a minimum threshold in both corpora
compact_corpus = corpus . compact ( st . ClassPercentageCompactor ( term_count = 2 ))
# Only take term coordinates of terms remaining in corpus
term_coordinates = term_coordinates . loc [ compact_corpus . get_terms ()]
html_compact = st . produce_scattertext_explorer (
compact_corpus ,
category = category_name ,
not_category_name = not_category_name ,
minimum_term_frequency = 0 ,
pmi_threshold_coefficient = 0 ,
width_in_pixels = 1000 ,
metadata = corpus . get_df ()[ 'speaker' ],
show_diagonal = True ,
original_y = term_coordinates [ category_name ],
original_x = term_coordinates [ not_category_name ],
x_coords = term_coordinates [ category_name + '_coord' ],
y_coords = term_coordinates [ not_category_name + '_coord' ],
max_overlapping = 3 ,
use_global_scale = True ,
get_tooltip_content = get_tooltip_content ,
) Occasionally, only term frequency statistics are available. This may happen in the case of very large, lost, or proprietary data sets. TermCategoryFrequencies is a corpus representation,that can accept this sort of data, along with any categorized documents that happen to be available.
Let use the Corpus of Contemporary American English as an example.
We'll construct a visualization to analyze the difference between spoken American English and English that occurs in fiction.
df = ( pd . read_excel ( 'https://www.wordfrequency.info/files/genres_sample.xls' )
. dropna ()
. set_index ( 'lemma' )[[ 'SPOKEN' , 'FICTION' ]]
. iloc [: 1000 ])
df . head ()
'''
SPOKEN FICTION
lemma
the 3859682.0 4092394.0
I 1346545.0 1382716.0
they 609735.0 352405.0
she 212920.0 798208.0
would 233766.0 229865.0
''' Transforming this into a visualization is extremely easy. Just pass a dataframe indexed on terms with columns indicating category-counts into the the TermCategoryFrequencies constructor.
term_cat_freq = st . TermCategoryFrequencies ( df ) And call produce_scattertext_explorer normally:
html = st . produce_scattertext_explorer (
term_cat_freq ,
category = 'SPOKEN' ,
category_name = 'Spoken' ,
not_category_name = 'Fiction' ,
) If you'd like to incorporate some documents into the visualization, you can add them into to the TermCategoyFrequencies object.
First, let's extract some example Fiction and Spoken documents from the sample COCA corpus.
import requests , zipfile , io
coca_sample_url = 'http://corpus.byu.edu/cocatext/samples/text.zip'
zip_file = zipfile . ZipFile ( io . BytesIO ( requests . get ( coca_sample_url ). content ))
document_df = pd . DataFrame (
[{ 'text' : zip_file . open ( fn ). read (). decode ( 'utf-8' ),
'category' : 'SPOKEN' }
for fn in zip_file . filelist if fn . filename . startswith ( 'w_spok' )][: 2 ]
+ [{ 'text' : zip_file . open ( fn ). read (). decode ( 'utf-8' ),
'category' : 'FICTION' }
for fn in zip_file . filelist if fn . filename . startswith ( 'w_fic' )][: 2 ]) And we'll pass the documents_df dataframe into TermCategoryFrequencies via the document_category_df parameter. Ensure the dataframe has two columns, 'text' and 'category'. Afterward, we can call produce_scattertext_explorer (or your visualization function of choice) normally.
doc_term_cat_freq = st . TermCategoryFrequencies ( df , document_category_df = document_df )
html = st . produce_scattertext_explorer (
doc_term_cat_freq ,
category = 'SPOKEN' ,
category_name = 'Spoken' ,
not_category_name = 'Fiction' ,
)Word representations have recently become a hot topic in NLP. While lots of work has been done visualizing how terms relate to one another given their scores (eg, http://projector.tensorflow.org/), none to my knowledge has been done visualizing how we can use these to examine how document categories differ.
In this example given a query term, "jobs", we can see how Republicans and Democrats talk about it differently.
In this configuration of Scattertext, words are colored by their similarity to a query phrase.
This is done using spaCy-provided GloVe word vectors (trained on the Common Crawl corpus). The cosine distance between vectors is used, with mean vectors used for phrases.
The calculation of the most similar terms associated with each category is a simple heuristic. First, sets of terms closely associated with a category are found. Second, these terms are ranked based on their similarity to the query, and the top rank terms are displayed to the right of the scatterplot.
A term is considered associated if its p-value is less than 0.05. P-values are determined using Monroe et al. (2008)'s difference in the weighted log-odds-ratios with an uninformative Dirichlet prior. This is the only model-based method discussed in Monroe et al. that does not rely on a large, in-domain background corpus. Since we are scoring bigrams in addition to the unigrams scored by Monroe, the size of the corpus would have to be larger to have high enough bigram counts for proper penalization. This function relies the Dirichlet distribution's parameter alpha, a vector, which is uniformly set to 0.01.
Here is the code to produce such a visualization.
>>> from scattertext import word_similarity_explorer
>>> html = word_similarity_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... target_term='jobs',
... minimum_term_frequency=5,
... pmi_threshold_coefficient=4,
... width_in_pixels=1000,
... metadata=convention_df['speaker'],
... alpha=0.01,
... max_p_val=0.05,
... save_svg_button=True)
>>> open("Convention-Visualization-Jobs.html", 'wb').write(html.encode('utf-8'))
Scattertext can interface with Gensim Word2Vec models. For example, here's a snippet from demo_gensim_similarity.py which illustrates how to train and use a word2vec model on a corpus. Note the similarities produced reflect quirks of the corpus, eg, "8" tends to refer to the 8% unemployment rate at the time of the convention.
import spacy
from gensim . models import word2vec
from scattertext import SampleCorpora , word_similarity_explorer_gensim , Word2VecFromParsedCorpus
from scattertext . CorpusFromParsedDocuments import CorpusFromParsedDocuments
nlp = spacy . en . English ()
convention_df = SampleCorpora . ConventionData2012 . get_data ()
convention_df [ 'parsed' ] = convention_df . text . apply ( nlp )
corpus = CorpusFromParsedDocuments ( convention_df , category_col = 'party' , parsed_col = 'parsed' ). build ()
model = word2vec . Word2Vec ( size = 300 ,
alpha = 0.025 ,
window = 5 ,
min_count = 5 ,
max_vocab_size = None ,
sample = 0 ,
seed = 1 ,
workers = 1 ,
min_alpha = 0.0001 ,
sg = 1 ,
hs = 1 ,
negative = 0 ,
cbow_mean = 0 ,
iter = 1 ,
null_word = 0 ,
trim_rule = None ,
sorted_vocab = 1 )
html = word_similarity_explorer_gensim ( corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
target_term = 'jobs' ,
minimum_term_frequency = 5 ,
pmi_threshold_coefficient = 4 ,
width_in_pixels = 1000 ,
metadata = convention_df [ 'speaker' ],
word2vec = Word2VecFromParsedCorpus ( corpus , model ). train (),
max_p_val = 0.05 ,
save_svg_button = True )
open ( './demo_gensim_similarity.html' , 'wb' ). write ( html . encode ( 'utf-8' ))How Democrats and Republicans talked differently about "jobs" in their 2012 convention speeches.
We can use Scattertext to visualize alternative types of word scores, and ensure that 0 scores are greyed out. Use the sparse_explroer function to acomplish this, and see its source code for more details.
>>> from sklearn.linear_model import Lasso
>>> from scattertext import sparse_explorer
>>> html = sparse_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... scores = corpus.get_regression_coefs('democrat', Lasso(max_iter=10000)),
... minimum_term_frequency=5,
... pmi_threshold_coefficient=4,
... width_in_pixels=1000,
... metadata=convention_df['speaker'])
>>> open('./Convention-Visualization-Sparse.html', 'wb').write(html.encode('utf-8'))
You can also use custom term positions and axis labels. For example, you can base terms' y-axis positions on a regression coefficient and their x-axis on term frequency and label the axes accordingly. The one catch is that axis positions must be scaled between 0 and 1.
First, let's define two scaling functions: scale to project positive values to [0,1], and zero_centered_scale project real values to [0,1], with negative values always <0.5, and positive values always >0.5.
>>> def scale(ar):
... return (ar - ar.min()) / (ar.max() - ar.min())
...
>>> def zero_centered_scale(ar):
... ar[ar > 0] = scale(ar[ar > 0])
... ar[ar < 0] = -scale(-ar[ar < 0])
... return (ar + 1) / 2.
Next, let's compute and scale term frequencies and L2-penalized regression coefficients. We'll hang on to the original coefficients and allow users to view them by mousing over terms.
>>> from sklearn.linear_model import LogisticRegression
>>> import numpy as np
>>>
>>> frequencies_scaled = scale(np.log(term_freq_df.sum(axis=1).values))
>>> scores = corpus.get_logreg_coefs('democrat',
... LogisticRegression(penalty='l2', C=10, max_iter=10000, n_jobs=-1))
>>> scores_scaled = zero_centered_scale(scores)
Finally, we can write the visualization. Note the use of the x_coords and y_coords parameters to store the respective coordinates, the scores and sort_by_dist arguments to register the original coefficients and use them to rank the terms in the right-hand list, and the x_label and y_label arguments to label axes.
>>> html = produce_scattertext_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... minimum_term_frequency=5,
... pmi_threshold_coefficient=4,
... width_in_pixels=1000,
... x_coords=frequencies_scaled,
... y_coords=scores_scaled,
... scores=scores,
... sort_by_dist=False,
... metadata=convention_df['speaker'],
... x_label='Log frequency',
... y_label='L2-penalized logistic regression coef')
>>> open('demo_custom_coordinates.html', 'wb').write(html.encode('utf-8'))
The Emoji analysis capability displays a chart of the category-specific distribution of Emoji. Let's look at a new corpus, a set of tweets. We'll build a visualization showing how men and women use emoji differently.
Note: the following example is implemented in demo_emoji.py .
First, we'll load the dataset and parse it using NLTK's tweet tokenizer. Note, install NLTK before running this example. It will take some time for the dataset to download.
import nltk , urllib . request , io , agefromname , zipfile
import scattertext as st
import pandas as pd
with zipfile . ZipFile ( io . BytesIO ( urllib . request . urlopen (
'http://followthehashtag.com/content/uploads/USA-Geolocated-tweets-free-dataset-Followthehashtag.zip'
). read ())) as zf :
df = pd . read_excel ( zf . open ( 'dashboard_x_usa_x_filter_nativeretweets.xlsx' ))
nlp = st . tweet_tokenzier_factory ( nltk . tokenize . TweetTokenizer ())
df [ 'parse' ] = df [ 'Tweet content' ]. apply ( nlp )
df . iloc [ 0 ]
'''
Tweet Id 721318437075685382
Date 2016-04-16
Hour 12:44
User Name Bill Schulhoff
Nickname BillSchulhoff
Bio Husband,Dad,GrandDad,Ordained Minister, Umpire...
Tweet content Wind 3.2 mph NNE. Barometer 30.20 in, Rising s...
Favs NaN
RTs NaN
Latitude 40.7603
Longitude -72.9547
Country US
Place (as appears on Bio) East Patchogue, NY
Profile picture http://pbs.twimg.com/profile_images/3788000007...
Followers 386
Following 705
Listed 24
Tweet language (ISO 639-1) en
Tweet Url http://www.twitter.com/BillSchulhoff/status/72...
parse Wind 3.2 mph NNE. Barometer 30.20 in, Rising s...
Name: 0, dtype: object
''' Next, we'll use the AgeFromName package to find the probabilities of the gender of each user given their first name. First, we'll find a dataframe indexed on first names that contains the probability that each someone with that first name is male ( male_prob ).
male_prob = agefromname . AgeFromName (). get_all_name_male_prob ()
male_prob . iloc [ 0 ]
'''
hi 1.00000
lo 0.95741
prob 1.00000
Name: aaban, dtype: float64
''' Next, we'll extract the first names of each user, and use the male_prob data frame to find users whose names indicate there is at least a 90% chance they are either male or female, label those users, and create new data frame df_mf with only those users.
df [ 'first_name' ] = df [ 'User Name' ]. apply ( lambda x : x . split ()[ 0 ]. lower () if type ( x ) == str and len ( x . split ()) > 0 else x )
df_aug = pd . merge ( df , male_prob , left_on = 'first_name' , right_index = True )
df_aug [ 'gender' ] = df_aug [ 'prob' ]. apply ( lambda x : 'm' if x > 0.9 else 'f' if x < 0.1 else '?' )
df_mf = df_aug [ df_aug [ 'gender' ]. isin ([ 'm' , 'f' ])] The key to this analysis is to construct a corpus using only the emoji extractor st.FeatsFromSpacyDocOnlyEmoji which builds a corpus only from emoji and not from anything else.
corpus = st . CorpusFromParsedDocuments (
df_mf ,
parsed_col = 'parse' ,
category_col = 'gender' ,
feats_from_spacy_doc = st . FeatsFromSpacyDocOnlyEmoji ()
). build () Next, we'll run this through a standard produce_scattertext_explorer visualization generation.
html = st . produce_scattertext_explorer (
corpus ,
category = 'f' ,
category_name = 'Female' ,
not_category_name = 'Male' ,
use_full_doc = True ,
term_ranker = st . OncePerDocFrequencyRanker ,
sort_by_dist = False ,
metadata = ( df_mf [ 'User Name' ]
+ ' (@' + df_mf [ 'Nickname' ] + ') '
+ df_mf [ 'Date' ]. astype ( str )),
width_in_pixels = 1000
)
open ( "EmojiGender.html" , 'wb' ). write ( html . encode ( 'utf-8' ))SentencePiece tokenization is a subword tokenization technique which relies on a language-model to produce optimized tokenization. It has been used in large, transformer-based contextual language models.
Ensure to run $ pip install sentencepiece before running this example.
First, let's load the political convention data set as normal.
import tempfile
import re
import scattertext as st
convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
convention_df [ 'parse' ] = convention_df . text . apply ( st . whitespace_nlp_with_sentences ) Next, let's train a SentencePiece tokenizer based on this data. The train_sentence_piece_tokenizer function trains a SentencePieceProcessor on the data set and returns it. You can of course use any SentencePieceProcessor.
def train_sentence_piece_tokenizer ( documents , vocab_size ):
'''
:param documents: list-like, a list of str documents
:vocab_size int: the size of the vocabulary to output
:return sentencepiece.SentencePieceProcessor
'''
import sentencepiece as spm
sp = None
with tempfile . NamedTemporaryFile ( delete = True ) as tempf :
with tempfile . NamedTemporaryFile ( delete = True ) as tempm :
tempf . write (( ' n ' . join ( documents )). encode ())
spm . SentencePieceTrainer . Train (
'--input=%s --model_prefix=%s --vocab_size=%s' % ( tempf . name , tempm . name , vocab_size )
)
sp = spm . SentencePieceProcessor ()
sp . load ( tempm . name + '.model' )
return sp
sp = train_sentence_piece_tokenizer ( convention_df . text . values , vocab_size = 2000 ) Next, let's add the SentencePiece tokens as metadata when creating our corpus. In order to do this, pass a FeatsFromSentencePiece instance into the feats_from_spacy_doc parameter. Pass the SentencePieceProcessor into the constructor.
corpus = st . CorpusFromParsedDocuments ( convention_df ,
parsed_col = 'parse' ,
category_col = 'party' ,
feats_from_spacy_doc = st . FeatsFromSentencePiece ( sp )). build ()Now we can create the SentencePiece token scatter plot.
html = st . produce_scattertext_explorer (
corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
sort_by_dist = False ,
metadata = convention_df [ 'party' ] + ': ' + convention_df [ 'speaker' ],
term_scorer = st . RankDifference (),
transform = st . Scalers . dense_rank ,
use_non_text_features = True ,
use_full_doc = True ,
)Suppose you'd like to audit or better understand weights or importances given to bag-of-words features by a classifier.
It's easy to use Scattertext to do, if you use a Scikit-learn-style classifier.
For example the Lighting package makes available high-performance linear classifiers which are have Scikit-compatible interfaces.
First, let's import sklearn 's text feature extraction classes, the 20 Newsgroup corpus, Lightning's Primal Coordinate Descent classifier, and Scattertext. We'll also fetch the training portion of the Newsgroup corpus.
from lightning . classification import CDClassifier
from sklearn . datasets import fetch_20newsgroups
from sklearn . feature_extraction . text import CountVectorizer , TfidfVectorizer
import scattertext as st
newsgroups_train = fetch_20newsgroups (
subset = 'train' ,
remove = ( 'headers' , 'footers' , 'quotes' )
)Next, we'll tokenize our corpus twice. Once into tfidf features which will be used to train the classifier, an another time into ngram counts that will be used by Scattertext. It's important that both vectorizers share the same vocabulary, since we'll need to apply the weight vector from the model onto our Scattertext Corpus.
vectorizer = TfidfVectorizer ()
tfidf_X = vectorizer . fit_transform ( newsgroups_train . data )
count_vectorizer = CountVectorizer ( vocabulary = vectorizer . vocabulary_ ) Next, we use the CorpusFromScikit factory to build a Scattertext Corpus object. Ensure the X parameter is a document-by-feature matrix. The argument to the y parameter is an array of class labels. Each label is an integer representing a different news group. We the feature_vocabulary is the vocabulary used by the vectorizers. The category_names are a list of the 20 newsgroup names which as a class-label list. The raw_texts is a list of the text of newsgroup texts.
corpus = st . CorpusFromScikit (
X = count_vectorizer . fit_transform ( newsgroups_train . data ),
y = newsgroups_train . target ,
feature_vocabulary = vectorizer . vocabulary_ ,
category_names = newsgroups_train . target_names ,
raw_texts = newsgroups_train . data
). build () Now, we can train the model on tfidf_X and the categoricla response variable, and capture feature weights for category 0 ("alt.atheism").
clf = CDClassifier ( penalty = "l1/l2" ,
loss = "squared_hinge" ,
multiclass = True ,
max_iter = 20 ,
alpha = 1e-4 ,
C = 1.0 / tfidf_X . shape [ 0 ],
tol = 1e-3 )
clf . fit ( tfidf_X , newsgroups_train . target )
term_scores = clf . coef_ [ 0 ]Finally, we can create a Scattertext plot. We'll use the Monroe-style visualization, and automatically select around 4000 terms that encompass the set of frequent terms, terms with high absolute scores, and terms that are characteristic of the corpus.
html = st . produce_frequency_explorer (
corpus ,
'alt.atheism' ,
scores = term_scores ,
use_term_significance = False ,
terms_to_include = st . AutoTermSelector . get_selected_terms ( corpus , term_scores , 4000 ),
metadata = [ '/' . join ( fn . split ( '/' )[ - 2 :]) for fn in newsgroups_train . filenames ]
)Let's take a look at the performance of the classifier:
newsgroups_test = fetch_20newsgroups ( subset = 'test' ,
remove = ( 'headers' , 'footers' , 'quotes' ))
X_test = vectorizer . transform ( newsgroups_test . data )
pred = clf . predict ( X_test )
f1 = f1_score ( pred , newsgroups_test . target , average = 'micro' )
print ( "Microaveraged F1 score" , f1 )Microaveraged F1 score 0.662108337759. Not bad over a ~0.05 baseline.
Please see Signo for an introduction to semiotic squares.
Some variants of the semiotic square-creator are can be seen in this notebook, which studies words and phrases in headlines that had low or high Facebook engagement and were published by either BuzzFeed or the New York Times: [http://nbviewer.jupyter.org/github/JasonKessler/PuPPyTalk/blob/master/notebooks/Explore-Headlines.ipynb]
The idea behind the semiotic square is to express the relationship between two opposing concepts and concepts things within a larger domain of a discourse. Examples of opposed concepts life or death, male or female, or, in our example, positive or negative sentiment. Semiotics squares are comprised of four "corners": the upper two corners are the opposing concepts, while the bottom corners are the negation of the concepts.
Circumscribing the negation of a concept involves finding everything in the domain of discourse that isn't associated with the concept. For example, in the life-death opposition, one can consider the universe of discourse to be all animate beings, real and hypothetical. The not-alive category will cover dead things, but also hypothetical entities like fictional characters or sentient AIs.
In building lexicalized semiotic squares, we consider concepts to be documents labeled in a corpus. Documents, in this setting, can belong to one of three categories: two labels corresponding to the opposing concepts, a neutral category, indicating a document is in the same domain as the opposition, but cannot fall into one of opposing categories.
In the example below positive and negative movie reviews are treated as the opposing categories, while plot descriptions of the same movies are treated as the neutral category.
Terms associated with one of the two opposing categories (relative only to the other) are listed as being associated with that category. Terms associated with a netural category (eg, not positive) are terms which are associated with the disjunction of the opposite category and the neutral category. For example, not-positive terms are those most associated with the set of negative reviews and plot descriptions vs. positive reviews.
Common terms among adjacent corners of the square are also listed.
An HTML-rendered square is accompanied by a scatter plot. Points on the plot are terms. The x-axis is the Z-score of the association to one of the opposed concepts. The y-axis is the Z-score how associated a term is with the neutral set of documents relative to the opposed set. A point's red-blue color indicate the term's opposed-association, while the more desaturated a term is, the more it is associated with the neutral set of documents.
Update to version 2.2: terms are colored by their nearest semiotic categories across the eight corresponding radial sectors.
import scattertext as st
movie_df = st . SampleCorpora . RottenTomatoes . get_data ()
movie_df . category = movie_df . category . apply
( lambda x : { 'rotten' : 'Negative' , 'fresh' : 'Positive' , 'plot' : 'Plot' }[ x ])
corpus = st . CorpusFromPandas (
movie_df ,
category_col = 'category' ,
text_col = 'text' ,
nlp = st . whitespace_nlp_with_sentences
). build (). get_unigram_corpus ()
semiotic_square = st . SemioticSquare (
corpus ,
category_a = 'Positive' ,
category_b = 'Negative' ,
neutral_categories = [ 'Plot' ],
scorer = st . RankDifference (),
labels = { 'not_a_and_not_b' : 'Plot Descriptions' , 'a_and_b' : 'Reviews' }
)
html = st . produce_semiotic_square_explorer ( semiotic_square ,
category_name = 'Positive' ,
not_category_name = 'Negative' ,
x_label = 'Fresh-Rotten' ,
y_label = 'Plot-Review' ,
neutral_category_name = 'Plot Description' ,
metadata = movie_df [ 'movie_name' ])There are a number of other types of semiotic square construction functions. Again, please see https://nbviewer.org/github/JasonKessler/PuPPyTalk/blob/master/notebooks/Explore-Headlines.ipynb for an overview of these.
A frequently requested feature of Scattertext has been the ability to visualize topic models. While this capability has existed in some forms (eg, the Empath visualization), I've finally gotten around to implementing a concise API for such a visualization. There are three main ways to visualize topic models using Scattertext. The first is the simplest: manually entering topic models and visualizing them. The second uses a Scikit-Learn pipeline to produce the topic models for visualization. The third is a novel topic modeling technique, based on finding terms similar to a custom set of seed terms.
If you have already created a topic model, simply structure it as a dictionary. This dictionary is keyed on string which serve as topic titles and are displayed in the main scatterplot. The values are lists of words that belong to that topic. The words that are in each topic list are bolded when they appear in a snippet.
Note that currently, there is no support for keyword scores.
For example, one might manually the following topic models to explore in the Convention corpus:
topic_model = {
'money' : [ 'money' , 'bank' , 'banks' , 'finances' , 'financial' , 'loan' , 'dollars' , 'income' ],
'jobs' : [ 'jobs' , 'workers' , 'labor' , 'employment' , 'worker' , 'employee' , 'job' ],
'patriotic' : [ 'america' , 'country' , 'flag' , 'americans' , 'patriotism' , 'patriotic' ],
'family' : [ 'mother' , 'father' , 'mom' , 'dad' , 'sister' , 'brother' , 'grandfather' , 'grandmother' , 'son' , 'daughter' ]
} We can use the FeatsFromTopicModel class to transform this topic model into one which can be visualized using Scattertext. This is used just like any other feature builder, and we pass the topic model object into produce_scattertext_explorer .
import scattertext as st
topic_feature_builder = st.FeatsFromTopicModel(topic_model)
topic_corpus = st.CorpusFromParsedDocuments(
convention_df,
category_col='party',
parsed_col='parse',
feats_from_spacy_doc=topic_feature_builder
).build()
html = st.produce_scattertext_explorer(
topic_corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
width_in_pixels=1000,
metadata=convention_df['speaker'],
use_non_text_features=True,
use_full_doc=True,
pmi_threshold_coefficient=0,
topic_model_term_lists=topic_feature_builder.get_top_model_term_lists()
)
Since topic modeling using document-level coocurence generally produces poor results, I've added a SentencesForTopicModeling class which allows clusterting by coocurence at the sentence-level. It requires a ParsedCorpus object to be passed to its constructor, and creates a term-sentence matrix internally.
Next, you can create a topic model dictionary like the one above by passing in a Scikit-Learn clustering or dimensionality reduction pipeline. The only constraint is the last transformer in the pipeline must populate a components_ attribute.
The num_topics_per_term attribute specifies how many terms should be added to a list.
In the following example, we'll use NMF to cluster a stoplisted, unigram corpus of documents, and use the topic model dictionary to create a FeatsFromTopicModel , just like before.
Note that in produce_scattertext_explorer , we make the topic_model_preview_size 20 in order to show a preview of the first 20 terms in the topic in the snippet view as opposed to the default 10.
from sklearn . decomposition import NMF
from sklearn . feature_extraction . text import TfidfTransformer
from sklearn . pipeline import Pipeline
convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
convention_df [ 'parse' ] = convention_df [ 'text' ]. apply ( st . whitespace_nlp_with_sentences )
unigram_corpus = ( st . CorpusFromParsedDocuments ( convention_df ,
category_col = 'party' ,
parsed_col = 'parse' )
. build (). get_stoplisted_unigram_corpus ())
topic_model = st . SentencesForTopicModeling ( unigram_corpus ). get_topics_from_model (
Pipeline ([
( 'tfidf' , TfidfTransformer ( sublinear_tf = True )),
( 'nmf' , ( NMF ( n_components = 100 , alpha = .1 , l1_ratio = .5 , random_state = 0 )))
]),
num_terms_per_topic = 20
)
topic_feature_builder = st . FeatsFromTopicModel ( topic_model )
topic_corpus = st . CorpusFromParsedDocuments (
convention_df ,
category_col = 'party' ,
parsed_col = 'parse' ,
feats_from_spacy_doc = topic_feature_builder
). build ()
html = st . produce_scattertext_explorer (
topic_corpus ,
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
width_in_pixels = 1000 ,
metadata = convention_df [ 'speaker' ],
use_non_text_features = True ,
use_full_doc = True ,
pmi_threshold_coefficient = 0 ,
topic_model_term_lists = topic_feature_builder . get_top_model_term_lists (),
topic_model_preview_size = 20
)A surprisingly easy way to generate good topic models is to use a term scoring formula to find words that are associated with sentences where a seed word occurs vs. where one doesn't occur.
Given a custom term list, the SentencesForTopicModeling.get_topics_from_terms will generate a series of topics. Note that the dense rank difference ( RankDifference ) works particularly well for this task, and is the default parameter.
term_list = [ 'obama' , 'romney' , 'democrats' , 'republicans' , 'health' , 'military' , 'taxes' ,
'education' , 'olympics' , 'auto' , 'iraq' , 'iran' , 'israel' ]
unigram_corpus = ( st . CorpusFromParsedDocuments ( convention_df ,
category_col = 'party' ,
parsed_col = 'parse' )
. build (). get_stoplisted_unigram_corpus ())
topic_model = ( st . SentencesForTopicModeling ( unigram_corpus )
. get_topics_from_terms ( term_list ,
scorer = st . RankDifference (),
num_terms_per_topic = 20 ))
topic_feature_builder = st . FeatsFromTopicModel ( topic_model )
# The remaining code is identical to two examples above. See demo_word_list_topic_model.py
# for the complete example. Scattertext makes it easy to create word-similarity plots using projections of word embeddings as the x and y-axes. In the example below, we create a stop-listed Corpus with only unigram terms. The produce_projection_explorer function by uses Gensim to create word embeddings and then projects them to two dimentions using Uniform Manifold Approximation and Projection (UMAP).
UMAP is chosen over T-SNE because it can employ the cosine similarity between two word vectors instead of just the euclidean distance.
convention_df = st . SampleCorpora . ConventionData2012 . get_data ()
convention_df [ 'parse' ] = convention_df [ 'text' ]. apply ( st . whitespace_nlp_with_sentences )
corpus = ( st . CorpusFromParsedDocuments ( convention_df , category_col = 'party' , parsed_col = 'parse' )
. build (). get_stoplisted_unigram_corpus ())
html = st . produce_projection_explorer ( corpus , category = 'democrat' , category_name = 'Democratic' ,
not_category_name = 'Republican' , metadata = convention_df . speaker ) In order to use custom word embedding functions or projection functions, pass models into the word2vec_model and projection_model parameters. In order to use T-SNE, for example, use projection_model=sklearn.manifold.TSNE() .
import umap
from gensim . models . word2vec import Word2Vec
html = st . produce_projection_explorer ( corpus ,
word2vec_model = Word2Vec ( size = 100 , window = 5 , min_count = 10 , workers = 4 ),
projection_model = umap . UMAP ( min_dist = 0.5 , metric = 'cosine' ),
category = 'democrat' ,
category_name = 'Democratic' ,
not_category_name = 'Republican' ,
metadata = convention_df . speaker ) Term positions can also be determined by the positions of terms according to the output of principal component analysis, and produce_projection_explorer also supports this functionality. We'll look at how axes transformations ("scalers" in Scattertext terminology) can make it easier to inspect the output of PCA.
We'll use the 2012 Conventions corpus for these visualizations. Only unigrams occurring in at least three documents will be considered.
>>> convention_df = st.SampleCorpora.ConventionData2012.get_data()
>>> convention_df['parse'] = convention_df['text'].apply(st.whitespace_nlp_with_sentences)
>>> corpus = (st.CorpusFromParsedDocuments(convention_df,
... category_col='party',
... parsed_col='parse')
... .build()
... .get_stoplisted_unigram_corpus()
... .remove_infrequent_words(minimum_term_count=3, term_ranker=st.OncePerDocFrequencyRanker))
Next, we use scikit-learn's tf-idf transformer to find very simple, sparse embeddings for all of these words. Since, we input a #docs x #terms matrix to the transformer, we can transpose it to get a proper term-embeddings matrix, where each row corresponds to a term, and the columns correspond to document-specific tf-idf scores.
>>> from sklearn.feature_extraction.text import TfidfTransformer
>>> embeddings = TfidfTransformer().fit_transform(corpus.get_term_doc_mat())
>>> embeddings.shape
(189, 2159)
>>> corpus.get_num_docs(), corpus.get_num_terms()
(189, 2159)
>>> embeddings = embeddings.T
>>> embeddings.shape
(2159, 189)
Given these spare embeddings, we can apply sparse singular value decomposition to extract three factors. SVD outputs factorizes the term embeddings matrix into three matrices, U, Σ, and VT. Importantly, the matrix U provides the singular values for each term, and VT provides them for each document, and Σ is a vector of the singular values.
>>> from scipy.sparse.linalg import svds
>>> U, S, VT = svds(embeddings, k = 3, maxiter=20000, which='LM')
>>> U.shape
(2159, 3)
>>> S.shape
(3,)
>>> VT.shape
(3, 189)
We'll look at the first two singular values, plotting each term such that the x-axis position is the first singular value, and the y-axis term is the second. To do this, we make a "projection" data frame, where the x and y columns store the first two singular values, and key the data frame on each term. This controls the term positions on the chart.
>>> x_dim = 0; y_dim = 1;
>>> projection = pd.DataFrame({'term':corpus.get_terms(),
... 'x':U.T[x_dim],
... 'y':U.T[y_dim]}).set_index('term')
We'll use the produce_pca_explorer function to visualize these. Note we include the projection object, and specify which singular values were used for x and y ( x_dim and y_dim ) so we they can be labeled in the interactive visualization.
html = st.produce_pca_explorer(corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
projection=projection,
metadata=convention_df['speaker'],
width_in_pixels=1000,
x_dim=x_dim,
y_dim=y_dim)
Click for an interactive visualization.
We can easily re-scale the plot in order to make more efficient use of space. For example, passing in scaler=scale_neg_1_to_1_with_zero_mean will make all four quadrants take equal area.
html = st.produce_pca_explorer(corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
projection=projection,
metadata=convention_df['speaker'],
width_in_pixels=1000,
scaler=st.scale_neg_1_to_1_with_zero_mean,
x_dim=x_dim,
y_dim=y_dim)
Click for an interactive visualization.
To export the content of a scattertext explorer object (ScattertextStructure) to matplotlib you can use produce_scattertext_pyplot . The function returns a matplotlib.figure.Figure object which can be visualized using plt.show or plt.savefig as in the example below.
Note that installation of textalloc==0.0.3 and matplotlib>=3.6.0 is required before running this.
convention_df = st.SampleCorpora.ConventionData2012.get_data().assign(
parse = lambda df: df.text.apply(st.whitespace_nlp_with_sentences)
)
corpus = st.CorpusFromParsedDocuments(convention_df, category_col='party', parsed_col='parse').build()
scattertext_structure = st.produce_scattertext_explorer(
corpus,
category='democrat',
category_name='Democratic',
not_category_name='Republican',
minimum_term_frequency=5,
pmi_threshold_coefficient=8,
width_in_pixels=1000,
return_scatterplot_structure=True,
)
fig = st.produce_scattertext_pyplot(scattertext_structure)
fig.savefig('pyplot_export.png', format='png')
[]
Please see the examples in the PyData 2017 Tutorial on Scattertext.
Cozy: The Collection Synthesizer (Loncaric 2016) was used to help determine which terms could be labeled without overlapping a circle or another label. It automatically built a data structure to efficiently store and query the locations of each circle and labeled term.
The script to build rectangle-holder.js was
fields ax1 : long, ay1 : long, ax2 : long, ay2 : long
assume ax1 < ax2 and ay1 < ay2
query findMatchingRectangles(bx1 : long, by1 : long, bx2 : long, by2 : long)
assume bx1 < bx2 and by1 < by2
ax1 < bx2 and ax2 > bx1 and ay1 < by2 and ay2 > by1
And it was called using
$ python2.7 src/main.py <script file name> --enable-volume-trees
--js-class RectangleHolder --enable-hamt --enable-arrays --js rectangle_holder.js
Adding in code to ensure that term statistics will show up even if no documents are present in visualization.
Better axis labeling (see demo_axis_crossbars_and_labels.py).
Pytextrank compatibility
Ensuring Pandas 1.0 compatibility fixing Issue #51 and scikit-learn stopwords import issue in #49.
AssociationCompactorByRank , TermCategoryRanker . terms_to_show parameter use_categories_as_metadata_and_replace_terms to TermDocMatrix .get_metadata_doc_count_df and get_metadata_count_mat to TermDocMatrix produce_pairplot ScatterChart.hide_terms(terms: iter[str]) which enables selected terms to be hidden from the chart.ScatterChartData.score_transform to specify the function which can change an original score into a value between 0 and 1 used for term coloring. alternative_term_func to produce_scattertext_explorer which allows you to inject a function that activates when a term is clicked.HedgesG , and unbiased version of Cohen's d which is a subclass of CohensD .frequency_transform parameter to produce_frequency_explorer . This defaults to a log transform, but allows you to use any way your heart desires to order terms along the x-axis. show_category_headings=True to produce_scattertext_explorer . Setting this to False suppresses the list of categories which will be displayed in the term context area.div_name argument to produce_scattertext_explorer and name-spaced important divs and classes by div_name in HTML templates and Javascript.show_cross_axes=True to produce_scattertext_explorer . Setting this to False prevents the cross axes from being displayed if show_axes is True .TermDocMatrix.get_metadata_freq_df now accepts the label_append argument which by default adds ' freq' to the end of each column.TermDocMatrix.get_num_cateogires returns the number of categories in a term-document matrix. Added the following methods:
TermDocMatrixWithoutCategories.get_num_metadataTermDocMatrix.use_metadata_as_categoriesunified_context argument in produce_scattertext_explorer lists all contexts in a single column. This let's you see snippets organized by multiple categories in a single column. See demo_unified_context.py for an example. Added a series of objects to handle uncategorized corpora. Added section on Document-Based Scatterplots, and the add_doc_names_as_metadata function. CategoryColorAssigner was also added to assign colors to a qualitative categories.
A number of new term scoring approaches including RelativeEntropy (a direct implementation of Frankhauser et al. ( 2014)), and ZScores and implementation of the Z-Score model used in Frankhauser et al.
TermDocMatrix.get_metadata_freq_df() returns a metadata-doc corpus.
CorpusBasedTermScorer.set_ranker allows you to use a different term ranker when finding corpus-based scores. This not only lets these scorers with metadata, but also allows you to integrate once-per-document counts.
Fixed produce_projection_explorer such that it can work with a predefined set of term embeddings. This can allow, for example, the easy exploration of one hot-encoded term embeddings in addition to arbitrary lower-dimensional embeddings.
Added add_metadata to TermDocMatrix in order to inject meta data after a TermDocMatrix object has been created.
Made sure tooltip never started above the top of the web page.
Added DomainCompactor .
Fixed bug #31, enabling context to show when metadata value is clicked.
Enabled display of terms in topic models in explorer, along with the the display of customized topic models. Please see Visualizing topic models for an overview of the additions.
Removed pkg_resources from Phrasemachine, corrected demo_phrase_machine.py
Now compatible with Gensim 3.4.0.
Added characteristic explorer, produce_characteristic_explorer , to plot terms with their characteristic scores on the x-axis and their class-association scores on the y-axis. See Ordering Terms by Corpus Characteristicness for more details.
Added TermCategoryFrequencies in response to Issue 23. Please see Visualizing differences based on only term frequencies for more details.
Added x_axis_labels and y_axis_labels parameters to produce_scattertext_explorer . These let you include evenly-spaced string axis labels on the chart, as opposed to just "Low", "Medium" and "High". These rely on d3's ticks function, which can behave unpredictable. Caveat usor.
Semiotic Squares now look better, and have customizable labels.
Incorporated the General Inquirer lexicon. For non-commercial use only. The lexicon is downloaded from their homepage at the start of each use. See demo_general_inquierer.py .
Incorporated Phrasemachine from AbeHandler (Handler et al. 2016). For the license, please see PhraseMachineLicense.txt . For an example, please see demo_phrase_machine.py .
Added CompactTerms for removing redundant and infrequent terms from term document matrices. These occur if a word or phrase is always part of a larger phrase; the shorter phrase is considered redundant and removed from the corpus. See demo_phrase_machine.py for an example.
Added FourSquare , a pattern that allows for the creation of a semiotic square with separate categories for each corner. Please see demo_four_square.py for an early example.
Finally, added a way to easily perform T-SNE-style visualizations on a categorized corpus. This uses, by default, the umap-learn package. Please see demo_tsne_style.py.
Fixed to ScaledFScorePresets(one_to_neg_one=True) , added UnigramsFromSpacyDoc .
Now, when using CorpusFromPandas , a CorpusDF object is returned, instead of a Corpus object. This new type of object keeps a reference to the source data frame, and returns it via the CorpusDF.get_df() method.
The factory CorpusFromFeatureDict was added. It allows you to directly specify term counts and metadata item counts within the dataframe. Please see test_corpusFromFeatureDict.py for an example.
Added a very semiotic square creator.
The idea to build a semiotic square that contrasts two categories in a Term Document Matrix while using other categories as neutral categories.
See Creating semiotic squares for an overview on how to use this functionality and semiotic squares.
Added a parameter to disable the display of the top-terms sidebar, eg, produce_scattertext_explorer(..., show_top_terms=False, ...) .
An interface to part of the subjectivity/sentiment dataset from Bo Pang and Lillian Lee. ``A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts''. ACL. 2004. See SampleCorpora.RottenTomatoes .
Fixed bug that caused tooltip placement to be off after scrolling.
Made category_name and not_category_name optional in produce_scattertext_explorer etc.
Created the ability to customize tooltips via the get_tooltip_content argument to produce_scattertext_explorer etc., control axes labels via x_axis_values and y_axis_values . The color_func parameter is a Javascript function to control color of a point. Function takes a parameter which is a dictionary entry produced by ScatterChartExplorer.to_dict and returns a string.
Integration with Scikit-Learn's text-analysis pipeline led the creation of the CorpusFromScikit and TermDocMatrixFromScikit classes.
The AutoTermSelector class to automatically suggest terms to appear in the visualization.
This can make it easier to show large data sets, and remove fiddling with the various minimum term frequency parameters.
For an example of how to use CorpusFromScikit and AutoTermSelector , please see demo_sklearn.py
Also, I updated the library and examples to be compatible with spaCy 2.
Fixed bug when processing single-word documents, and set the default beta to 2.
Added produce_frequency_explorer function, and adding the PEP 369-compliant __version__ attribute as mentioned in #19. Fixed bug when creating visualizations with more than two possible categories. Now, by default, category names will not be title-cased in the visualization, but will retain their original case.
If you'd still like to do this this, use ScatterChart (or a descendant).to_dict(..., title_case_names=True) . Fixed DocsAndLabelsFromCorpus for Py 2 compatibility.
Fixed bugs in chinese_nlp when jieba has already been imported and in p-value computation when performing log-odds-ratio w/ prior scoring.
Added demo for performing a Monroe et. al (2008) style visualization of log-odds-ratio scores in demo_log_odds_ratio_prior.py .
Breaking change: pmi_filter_thresold has been replaced with pmi_threshold_coefficient .
Added Emoji and Tweet analysis. See Emoji analysis.
Characteristic terms falls back ot "Most frequent" if no terms used in the chart are present in the background corpus.
Fixed top-term calculation for custom scores.
Set scaled f-score's default beta to 0.5.
Added --spacy_language_model argument to the CLI.
Added the alternative_text_field option in produce_scattertext_explorer to show an alternative text field when showing contexts in the interactive HTML visualization.
Updated ParsedCorpus.get_unigram_corpus to allow for continued alternative_text_field functionality.
Added ability to for Scattertext to use noun chunks instead of unigrams and bigrams through the FeatsFromSpacyDocOnlyNounChunks class. In order to use it, run your favorite Corpus or TermDocMatrix factory, and pass in an instance of the class as a parameter:
st.CorpusFromParsedDocuments(..., feats_from_spacy_doc=st.FeatsFromSpacyDocOnlyNounChunks())
Fixed a bug in corpus construction that occurs when the last document has no features.
Now you don't have to install tinysegmenter to use Scattertext. But you need to install it if you want to parse Japanese. This caused a problem when Scattertext was being installed on Windows.
Added TermDocMatrix.get_corner_score , giving an improved version of the Rudder Score. Exposing whitespace_nlp_with_sentences . It's a lightweight bad regex sentence splitter built a top a bad regex tokenizer that somewhat apes spaCy's API. Use it if you don't have spaCy and the English model downloaded or if you care more about memory footprint and speed than accuracy.
It's not compatible with word_similarity_explorer but is compatible with `word_similarity_explorer_gensim'.
Tweaked scaled f-score normalization.
Fixed Javascript bug when clicking on '$'.
Fixed bug in Scaled F-Score computations, and changed computation to better score words that are inversely correlated to category.
Added Word2VecFromParsedCorpus to automate training Gensim word vectors from a corpus, and
word_similarity_explorer_gensim to produce the visualization.
See demo_gensim_similarity.py for an example.
Added the d3_url and d3_scale_chromatic_url parameters to produce_scattertext_explorer . This provides a way to manually specify the paths to "d3.js" (ie, the file from "https://cdnjs.cloudflare.com/ajax/libs/d3/4.6.0/d3.min.js") and "d3-scale-chromatic.v1.js" (ie, the file from "https://d3js.org/d3-scale-chromatic.v1.min.js").
This is important if you're getting the error:
Javascript error adding output!
TypeError: d3.scaleLinear is not a function
See your browser Javascript console for more details.
It also lets you use Scattertext if you're serving in an environment with no (or a restricted) external Internet connection.
For example, if "d3.min.js" and "d3-scale-chromatic.v1.min.js" were present in the current working directory, calling the following code would reference them locally instead of the remote Javascript files. See Visualizing term associations for code context.
>>> html = st.produce_scattertext_explorer(corpus,
... category='democrat',
... category_name='Democratic',
... not_category_name='Republican',
... width_in_pixels=1000,
... metadata=convention_df['speaker'],
... d3_url='d3.min.js',
... d3_scale_chromatic_url='d3-scale-chromatic.v1.min.js')
Fixed a bug in 0.0.2.6.0 that transposed default axis labels.
Added a Japanese mode to Scattertext. See demo_japanese.py for an example of how to use Japanese. Please run pip install tinysegmenter to parse Japanese.
Also, the chiense_mode boolean parameter in produce_scattertext_explorer has been renamed to asian_mode .
For example, the output of demo_japanese.py is:
Custom term positions and axis labels. Although not recommended, you can visualize different metrics on each axis in visualizations similar to Monroe et al. (2008). Please see Custom term positions for more info.
Enhanced the visualization of query-based categorical differences, aka the word_similarity_explorer function. When run, a plot is produced that contains category associated terms colored in either red or blue hues, and terms not associated with either class colored in greyscale and slightly smaller. The intensity of each color indicates association with the query term. Par exemple:
Some minor bug fixes, and added a minimum_not_category_term_frequency parameter. This fixes a problem with visualizing imbalanced datasets. It sets a minimum number of times a word that does not appear in the target category must appear before it is displayed.
Added TermDocMatrix.remove_entity_tags method to remove entity type tags from the analysis.
Fixed matched snippet not displaying issue #9, and fixed a Python 2 issue in created a visualization using a ParsedCorpus prepared via CorpusFromParsedDocuments , mentioned in the latter part of the issue #8 discussion.
Again, Python 2 is supported in experimental mode only.
Corrected example links on this Readme.
Fixed a bug in Issue 8 where the HTML visualization produced by produce_scattertext_html would fail.
Fixed a couple issues that rendered Scattertext broken in Python 2. Chinese processing still does not work.
Note: Use Python 3.4+ if you can.
Fixed links in Readme, and made regex NLP available in CLI.
Added the command line tool, and fixed a bug related to Empath visualizations.
Ability to see how a particular term is discussed differently between categories through the word_similarity_explorer function.
Specialized mode to view sparse term scores.
Fixed a bug that was caused by repeated values in background unigram counts.
Added true alphabetical term sorting in visualizations.
Added an optional save-as-SVG button.
Addition option of showing characteristic terms (from the full set of documents) being considered. The option ( show_characteristic in produce_scattertext_explorer ) is on by default, but currently unavailable for Chinese. If you know of a good Chinese wordcount list, please let me know. The algorithm used to produce these is F-Score.
See this and the following slide for more details
Added document and word count statistics to main visualization.
Added preliminary support for visualizing Empath (Fast 2016) topics categories instead of emotions. See the tutorial for more information.
Improved term-labeling.
Addition of strip_final_period param to FeatsFromSpacyDoc to deal with spaCy tokenization of all-caps documents that can leave periods at the end of terms.
I've added support for Chinese, including the ChineseNLP class, which uses a RegExp-based sentence splitter and Jieba for word segmentation. To use it, see the demo_chinese.py file. Note that CorpusFromPandas currently does not support ChineseNLP.
In order for the visualization to work, set the asian_mode flag to True in produce_scattertext_explorer .
