This is a consolidated library for joint topic-sentiment models.
Joint topic-sentiment models extract topical as well as sentiment information for each text. This library contains 4 different jst models - JST, RJST, TSM and sLDA.
References -
[1] https://www.researchgate.net/figure/JST-and-Reverse-JST-sentiment-classification-results-with-multiple-topics_fig1_47454505
[2] https://www.aaai.org/ocs/index.php/AAAI/AAAI10/paper/viewFile/1913/2215
[3] https://hal.archives-ouvertes.fr/hal-02052354/document
[4] https://github.com/ayushjain91/Sentiment-LDA
[5] https://gist.github.com/mblondel/542786
git clone https://github.com/victor7246/jointtsmodel.git
cd jointtsmodel
python setup.py install
Or from pip:
pip install jointtsmodel
We can use vectorized texts to run joint topic-sentiment models.
from jointtsmodel.RJST import RJST
from jointtsmodel.JST import JST
from jointtsmodel.sLDA import sLDA
from jointtsmodel.TSM import TSM
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.datasets import fetch_20newsgroups
from jointtsmodel.utils import *
# This produces a feature matrix of token counts, similar to what
# CountVectorizer would produce on text.
data, _ = fetch_20newsgroups(shuffle=True, random_state=1,
remove=('headers', 'footers', 'quotes'),
return_X_y=True)
data = data[:1000]
vectorizer = CountVectorizer(max_df=0.7, min_df=10,
max_features=5000,
stop_words='english')
X = vectorizer.fit_transform(data)
vocabulary = vectorizer.get_feature_names()
inv_vocabulary = dict(zip(vocabulary,np.arange(len(vocabulary))))
lexicon_data = pd.read_excel('lexicon/prior_sentiment.xlsx')
lexicon_data = lexicon_data.dropna()
lexicon_dict = dict(zip(lexicon_data['Word'],lexicon_data['Sentiment']))
For JST model use
model = JST(n_topic_components=5,n_sentiment_components=5,random_state=123,evaluate_every=2)
model.fit(X.toarray(), lexicon_dict)
model.transform()[:2]
top_words = list(model.getTopKWords(vocabulary).values())
top_words_by_topic = list(model.getTopKWordsByTopic(vocabulary).values())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words_by_topic)
Hscore(model.transform())
For RJST model use
model = RJST(n_topic_components=5,n_sentiment_components=5,random_state=123,evaluate_every=2)
model.fit(X.toarray(), lexicon_dict)
model.transform()[:2]
top_words = list(model.getTopKWords(vocabulary).values())
top_words_by_topic = list(model.getTopKWordsByTopic(vocabulary).values())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words_by_topic)
Hscore(model.transform())
For TSM use
model = TSM(n_topic_components=5,n_sentiment_components=5,random_state=123,evaluate_every=2)
model.fit(X.toarray(), lexicon_dict)
model.transform()[:2]
top_words = list(model.getTopKWords(vocabulary).values())
top_words_by_topic = list(model.getTopKWordsByTopic(vocabulary).values())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words_by_topic)
Hscore(model.transform())
For sLDA model use
model = sLDA(n_topic_components=5,n_sentiment_components=5,random_state=123,evaluate_every=2)
model.fit(X.toarray(), vocabulary)
model.transform()[:2]
top_words = list(model.getTopKWords(vocabulary).values())
top_words_by_topic = list(model.getTopKWordsByTopic(vocabulary).values())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words_by_topic)
Hscore(model.transform())
- Add parallelization for faster execution
- Handle sparse matrix
- Add online jst models
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