Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61638
Title: Featuring, detecting, and visualizing human sentiment in Chinese micro-blog
Authors: Yu, Z
Wang, Z 
Chen, L
Guo, B
Li, W 
Keywords: Rule set-based model
Sentiment detection
Sentiment lexicon expansion
Visualization
Issue Date: 2016
Publisher: Association for Computing Machinary
Source: ACM transactions on knowledge discovery from data, 2016, v. 10, no. 4, 48 How to cite?
Journal: ACM transactions on knowledge discovery from data 
Abstract: Micro-blog has been increasingly used for the public to express their opinions, and for organizations to detect public sentiment about social events or public policies. In this article, we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, multi-media elements, and hierarchical structure of Chinese "Weibo." Based on the analysis, we propose a novel approach and develop associated theoretical and technological methods toaddress these problems. These include a new sentiment word mining method based on three wording metrics and point-wise information, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements as auxiliary affective factors. We evaluate our new word discovery and sentiment detection methods on a real-life Chinese micro-blog dataset. Initial results show that our new diction can improve sentiment detection, and they demonstrate that our multi-level rule set methodis more effective, with the average accuracy being 10.2% and 1.5% higher than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualization techniques to study the relationships between online sentiment and real life. The visualization of detected sentiment can help depict temporal patterns and spatial discrepancy.
URI: http://hdl.handle.net/10397/61638
ISSN: 1556-4681 (print)
1556-472X (online)
DOI: 10.1145/2821513
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