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 language | English |
|---|---|
| Article number | e0223317 |
| Journal | PLoS ONE |
| Volume | 14 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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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 journal › Article
}
TY - JOUR
T1 - Refined distributed emotion vector representation for social media sentiment analysis
AU - Chang, Yung Chun
AU - Yeh, Wen Chao
AU - Hsing, Yan Chun
AU - Wang, Chen Ann
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0223317
DO - 10.1371/journal.pone.0223317
M3 - Article
C2 - 31647844
AN - SCOPUS:85074062989
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 10
M1 - e0223317
ER -