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
N1 - Funding Information:
This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 106-2218-E-038-004-MY2, MOST 107-2410-H-038-017-MY3, and MOST 107-2634-F-001-005. The Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan also provided financial support for our work. This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 106-2218-E-038-004-MY2, MOST 107-2410-H-038-017-MY3, and MOST 107- 2634-F-001-005. The Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan also provided financial support for our work. Moreover, we are grateful to Mr. Lin Zheng-Wen for his collaboration during preliminary investigations.
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 -