Package: jointtsmodel
jointtsmodelThis is a consolidated library for joint topic-sentiment (jst) models.DescriptionJoint topic-sentiment models extract topical as well as sentiment information for each text. This library contains different jst models - JST, RJST, TSM, sLDA and TSWE.Installationgit clone https://github.com/victor7246/jointtsmodel.git
cd jointtsmodel
python setup.py installOr from pip:pip install jointtsmodelUsageWe 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
from jointtsmodel.TSWE import TSWE

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 usemodel = 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())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words)
Hscore(model.transform())For RJST model usemodel = 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())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words)
Hscore(model.transform())For TSM usemodel = 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())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words)
Hscore(model.transform())For sLDA model usemodel = 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())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words)
Hscore(model.transform())For TSWE model we need word embedding matrix as an input.embeddings_index = {}
f = open('embeddings/glove.6B.100d.txt','r',encoding='utf8')

for i, line in enumerate(f):
    values = line.split()
    word = values[0]
    coefs = np.asarray(values[1:], dtype='float32')
    embeddings_index[word] = coefs
f.close()

print('Found %s word vectors.' % len(embeddings_index))

embedding_matrix = np.zeros((X.shape[1], 100))

for i, word in enumerate(vocabulary):
    if word in embeddings_index:
        embedding_matrix[i] = embeddings_index[word]
    else:
        embedding_matrix[i] = np.zeros(100)Run TSWE modelmodel = TSWE(embedding_dim=100,n_topic_components=5,n_sentiment_components=5,random_state=123,evaluate_every=2)
model.fit(X.toarray(), lexicon_dict, embedding_matrix)

model.transform()[:2]

top_words = list(model.getTopKWords(vocabulary).values())
coherence_score_uci(X.toarray(),inv_vocabulary,top_words)
Hscore(model.transform())To doAdd parallelization for faster executionHandle sparse matrixAdd online JST modelsReferences -[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[6]http://ceur-ws.org/Vol-1646/paper6.pdf