NDLI: Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

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Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

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Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

Content Provider	ACM Digital Library
Author	Li, Xi Wang, Hanqi Wu, Fei Zhuang, Yueting Shao, Jian Tang, Siliang
Abstract	The ever-increasing user-generated contents in social media and other web services make it highly desirable to discover opinions of users on all kinds of topics. Motivated by the assumption that individual word and paragraph in documents will deliver fine-grained (e.g., "laudatory", "annoyed" or "boring") and coarse-grained (e.g., positive, negative or neutral) sentiments about certain topics respectively, this paper focuses on a deeper thematic level to jointly disentangle fine-grained and coarse-grained opinions towards topics in terms of sentiment analysis, named as LDA with multi-grained sentiments (MgS-LDA). As a result, the proposed MgS-LDA not only discovers the topics in social media, but also identifies opinions about a given topic in terms of fine-grained and coarse-grained sentiment. Results of several experiments show that our proposed MgS-LDA achieves better performance on both sentimental classification and topic modeling than related methods.
Starting Page	913
Ending Page	916
Page Count	4
File Format	PDF
ISBN	9781450330633
DOI	10.1145/2647868.2655067
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Topic modeling Sentiment analysis Fine-grained sentiments
Content Type	Text
Resource Type	Article

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