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A C++ implementation of the Joint Sentiment-Topic (JST) Model.
This project forked from linron84/jst
A C++ implementation of the Joint Sentiment-Topic (JST) Model.
***************************************************** Joint Sentiment-Topic (JST) Model ***************************************************** (C) Copyright 2013, Chenghua Lin and Yulan He Written by Chenghua Lin, University of Aberdeen, [email protected], part of code is from http://gibbslda.sourceforge.net/. This file is part of JST implementation. JST is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. JST is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ------------------------------------------------------------------------ This is a C++ implementation of the joint sentiment-topic (JST) model for sentiment classification and extracting sentiment-bearing topics from text copara. ------------------------------------------------------------------------ TABLE OF CONTENTS A. COMPILING B. ESTIMATION C. INFERENCE D. Data format E. References ------------------------------------------------------------------------ A. COMPILING Type "make" in a shell. ------------------------------------------------------------------------ B. ESTIMATION Estimate the model by executing: jst -est -config YOUR-PATH/train.properties Outputs of jst estimation include the following files: <iter>.others // contains model parameter settings <iter>.pi // contains the per-document sentiment distributions <iter>.phi // contains the sentiment specific topic-word distributions <iter>.theta // contains the per-document sentiment specific topic proportions <iter>.tassign // contains the sentiment label and topic assignments for words in training data ------------------------------------------------------------------------ C. INFERENCE To perform inference on a different set of data (in the same format as for estimation), execute: jst -inf -config YOUR-PATH/test.properties Outputs of jst inference include the following files: <modelName_iter>.newothers <modelName_iter>.newpi <modelName_iter>.newphi <modelName_iter>.newtheta <modelName_iter>.newtassign ------------------------------------------------------------------------ D. Data format (1) The input data format for estimation/inference is as follows, where each line is one document, preceded by the document ID. [Doc_1 name] [token_1] [token_2] ... [token_N] : : [Doc_M name] [token_1] [token_2] ... [token_N] (2) Sentiment lexicon (mpqa.constraint) [word] [neu prior prob.] [pos prior prob.] [neg prior prob.] ------------------------------------------------------------------------ E. References [1] Lin, C., He, Y., Everson, R. and Reuger, S. Weakly-supervised Joint Sentiment-Topic Detection from Text, IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011. [2] Lin, C. and He, Y. Joint Sentiment/Topic Model for Sentiment Analysis, In Proceedings of the 18th ACM Conference on Information and Knowl- edge Management (CIKM), Hong Kong, China, 2009.
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