Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

Zhiwen Yu, zhitao Wang, Liming (Luke) Chen, Bin Guo, Wenjie Li

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
152 Downloads (Pure)


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 to address 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 method is 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.
Original languageEnglish
Article number48
Pages (from-to)1-23
Number of pages23
JournalACM Transactions on Knowledge Discovery from Data
Issue number4
Publication statusPublished (in print/issue) - 31 May 2016


Dive into the research topics of 'Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog'. Together they form a unique fingerprint.

Cite this