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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (2): 98-107    DOI: 10.11925/infotech.2096-3467.2018.0578
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Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text
Cuiqing Jiang1,2(),Yibo Guo1,Yao Liu1
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of Education, Hefei 230009, China
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Abstract  

[Objective] This study aims to construct a domain sentiment lexicon by discovering unrecognized sentiment words from user-generated contents on Chinese social media to apply it to automotive comments sentiment analysis. [Methods] First, words in HowNet are selected as the seeds, and PMI and Word2Vec algorithm are used to calculate the sentiment polarity of the candidates respectively on real automative corpus. Then the results of the two discriminations are judged synthetically according to the ensemble rules. Finally the proposed method was shown effective by the comparison of the sentiment classification experiments. [Results] The accuracy rate of the lexicon constructed according to proposed method is 21.6% higher than that of HowNet. The lexicon constructed by PMI and Word2Vec respectively increase 3.7% and 2.1%. Meanwhile the number of positive and negative emotional words are greatly increased. [Limitations] The source of corpus is single, and it has certain limitations in guiding other fields. [Conclusions] The sentiment lexicon constructed by this method can be applied to sentiment analysis of social media texts effectively.

Key wordsSocial Media      Sentiment Analysis      Sentiment Lexicon      PMI      Word2Vec     
Received: 23 May 2018      Published: 27 March 2019

Cite this article:

Cuiqing Jiang,Yibo Guo,Yao Liu. Constructing a Domain Sentiment Lexicon Based on Chinese Social Media Text. Data Analysis and Knowledge Discovery, 2019, 3(2): 98-107.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0578     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I2/98

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