SegmentLevelJointTopicSentimentModel

A C/C++ implementation of Segment-Level Joint Topic-Sentiment Model (STSM) using Gibbss Sampling for parameter estimation.

Yang Q, Rao Y, Xie H, et al. Segment-Level Joint Topic-Sentiment Model for Online Review Analysis[J]. IEEE Intelligent Systems, 2019, 34(1): 43-50.

Compile

A C/C++11.0 compiler and the STL library. In the Makefile, we use g++ as the default compiler command, if the C/C++ compiler on your system has another name, you can modify the CC variable in the Makefile.

Go to home directory and type:

$ make clean
$ make all

Usage

$ main [-ntopics <int>] [-niters <int>] [-nsentis <int>] -doc_dir <string> -output_dir <string> [-lexicon_dir <string>]

in which (parameters in [] are optional):

• -ntopics : The number of topics. Default value is 100.

• -niters : The iteration steps. Default value is 1000.

• -nsentis : The number of sentiments. Default value is 2.

• -doc_dir : Directory of input training data file, where contains two necessary files. The first file name is "DocumentId.txt", which is the ids for each documents. Each line in this file is a document where sentences are split by two tabs <\t\t>, segments are split by one tab <\t> and words in segment are split by a space . The second file name is "vocabulary.txt", which is the vocabulary word string. Each line inside this file is a word string.

• -lexicon_dir : Directory of the sentiment lexicon files. Each file has the name "SentiWords_x" where x is 0,1,2... Each line is a word.

• -output_dir : Directory of the output files.

  • assignment_<model_name>: topic and sentiment assignment for every documents.

  • phi_<model_name>: topic-sentiment-word distribution

  • pi_<model_name>: document-sentiment distribution

  • theta_<model_name>: document-topic distribution

  • topwords<model_name>: top k words in each topic-sentiment

  • model_name is in the format:

    ntopics<int>_nsentis<int>(<int>)_niters<int>_alpha<double>gamma<double>_betas<double/double/double>

    nsentis(): first int is the setting nsentis for model parameter, and second int represents how many sentiments are used for lexicon.