[1]WU Zhongqiang,ZHANG Yaowen,SHANG Lin.Multi-view sentiment classification of microblogs based on semantic features[J].CAAI Transactions on Intelligent Systems,2017,12(5):745-751.[doi:10.11992/tis.201706026]
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Multi-view sentiment classification of microblogs based on semantic features

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 745-751 Column: 学术论文—自然语言处理与理解 Public date: 2017-10-25
Title:
Multi-view sentiment classification of microblogs based on semantic features
Author(s):
WU Zhongqiang1; 2;  ZHANG Yaowen1; 2;  SHANG Lin1; 2
1. State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210046, China;
2. Department of Computer Science and Technology, Nanjing University, Nanjing 210046, China
Keywords:
sentiment analysis; text mining; latent semantic analysis; multi-view; semantic features; feature fusion; feature extraction
CLC:
TP181
DOI:
10.11992/tis.201706026
Abstract:
The objective in sentiment analysis is to analyze the sentiment tendency contained in subjective text. Most sentiment analysis methods deal with text only and ignore the information provided in the corresponding pictures. In this paper, we propose a multi-view microblog analysis method based on semantic features. Using latent semantic analysis, we map both the text and image features to the semantic space in the same dimensionality, and use SVM-2K to obtain and classify the respective semantic features. We conducted experiments by crawling text and pictures from popular microblogs. The results show that, by combining the semantic features of text and pictures, the sentiment classification result is better than that obtained using text or image features alone.
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