Refined distributed emotion vector representation for social media sentiment analysis

Yung Chun Chang, Wen Chao Yeh, Yan Chun Hsing, Chen Ann Wang

Research output: Contribution to journalArticle

Abstract

As user-generated content increasingly proliferates through social networking sites, our lives are bombarded with ever more information, which has in turn has inspired the rapid evolution of new technologies and tools to process these vast amounts of data. Semantic and sentiment analysis of these social multimedia have become key research topics in many areas in society, e.g., in shopping malls to help policymakers predict market trends and discover potential customers. In this light, this study proposes a novel method to analyze the emotional aspects of Chinese vocabulary and then to assess the mass comments of the movie reviews. The experiment results show that our method 1. can improve the machine learning model by providing more refined emotional information to enhance the effectiveness of movie recommendation systems, and 2. performs significantly better than the other commonly used methods of emotional analysis.

Original languageEnglish
Article numbere0223317
JournalPLoS ONE
Volume14
Issue number10
DOIs
Publication statusPublished - Jan 1 2019

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Social Media
Shopping centers
social networks
Recommender systems
emotions
Learning systems
Emotions
Semantics
Motion Pictures
Social Networking
Multimedia
Vocabulary
artificial intelligence
Experiments
methodology
markets
Technology
Research

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

Refined distributed emotion vector representation for social media sentiment analysis. / Chang, Yung Chun; Yeh, Wen Chao; Hsing, Yan Chun; Wang, Chen Ann.

In: PLoS ONE, Vol. 14, No. 10, e0223317, 01.01.2019.

Research output: Contribution to journalArticle

Chang, Yung Chun ; Yeh, Wen Chao ; Hsing, Yan Chun ; Wang, Chen Ann. / Refined distributed emotion vector representation for social media sentiment analysis. In: PLoS ONE. 2019 ; Vol. 14, No. 10.
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