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Joint Sentiment-Topic (JST) Model
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(C) Copyright 2013, Chenghua Lin and Yulan He
Written by Chenghua Lin, University of Aberdeen, chenghua.lin@abdn.ac.uk, 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
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This is a C++ implementation of the joint sentiment-topic (JST) model for
sentiment classification and extracting sentiment-bearing topics from text copara.
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TABLE OF CONTENTS
A. COMPILING
B. ESTIMATION
C. INFERENCE
D. Data format
E. References
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A. COMPILING
Type "make" in a shell.
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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
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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
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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.]
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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.