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Learning from context: a mutual reinforcement model for Chinese microblog opinion retrieval |
Jingjing WEI1,2,3, Xiangwen LIAO1,3( ), Houdong ZHENG1,3, Guolong CHEN1,3, Xueqi CHENG4 |
1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China 2. College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou 350108, China 3. Fujian Provincial Key Laboratory of Network Computing and Intelligence Processing, Fuzhou University, Fuzhou 350108, China 4. Institute of Computing Technology, Chinese Academic of Sciences, Beijing 100190, China |
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Abstract This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentimentwords relying on the document features. However, this approach could not be effectively applied to microblogs that have typical user-generated content with valuable contextual information: “user–user” interpersonal interactions and “user–post/comment” intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.
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Keywords
opinion retrieval
sentiment words
lexicon weighting
mutual reinforcement model
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Corresponding Author(s):
Xiangwen LIAO
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Just Accepted Date: 30 September 2016
Online First Date: 12 December 2017
Issue Date: 14 June 2018
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