This is a java implementation of Joint Sentiment Topic Model. JST can be used for sentiment analysis and emotion detection.
Method 1: Compile a jar using the jst.core.JST class as Main and execute.
Method 2: Run jst.core.Run.
Following is the extracted topics using a chinese news dataset. Check the .stwords file in model directory to see all the topics.
The format of sentiment or emotion lexicon file is as follows:
S senti_name_1 senti_name_2 ... senti_name_S
token_1 token_sentiment_distribution_1
token_2 token_sentiment_distribution_2
.
.
token_m sentiment_distribution_m
where S is the number of sentiment and token sentiment distribution is a S-dimensional vector separated by a blank.
Refer to lexicon.txt in the data directory.
N
doc_sentiment_distribution_1#word_1 word_2 ... word_d1
doc_sentiment_distribution_2#word_1 word_2 ... word_d2
.
.
doc_sentiment_distribution_N#word_1 word_2 ... word_dN
where N is the number of documents, document sentiment distribution is a S-dimensional vector separated by a blank.
A demo chinese dataset has been provided in the data directory. Segmentation of Chinese text or tokenization of English text should be done for preprocessing. Run jst.core.Run to train a new model.
- zhikai.zhang
- Email zhangzhikai@seu.edu.cn
- Blog http://zhikaizhang.cn
