文章基本信息

标题：Incorporating Background Checks with Sentiment Analysis to Identify Violence Risky Chinese Microblogs
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作者：Yun-Fei Jia ; Shan Li ; Renbiao Wu 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2019
卷号：11
期号：9
页码：200-212
DOI：10.3390/fi11090200
出版社：MDPI Publishing
摘要：Based on Web 2.0 technology, more and more people tend to express their attitude or opinions on the Internet. Radical ideas, rumors, terrorism, or violent contents are also propagated on the Internet, causing several incidents of social panic every year in China. In fact, most of this content comprises joking or emotional catharsis. To detect this with conventional techniques usually incurs a large false alarm rate. To address this problem, this paper introduces a technique that combines sentiment analysis with background checks. State-of-the-art sentiment analysis usually depends on training datasets in a specific topic area. Unfortunately, for some domains, such as violence risk speech detection, there is no definitive training data. In particular, topic-independent sentiment analysis of short Chinese text has been rarely reported in the literature. In this paper, the violence risk of the Chinese microblogs is calculated from multiple perspectives. First, a lexicon-based method is used to retrieve violence-related microblogs, and then a similarity-based method is used to extract sentiment words. Semantic rules and emoticons are employed to obtain the sentiment polarity and sentiment strength of short texts. Second, the activity risk is calculated based on the characteristics of part of speech (PoS) sequence and by semantic rules, and then a threshold is set to capture the key users. Finally, the risk is confirmed by historical speeches and the opinions of the friend-circle of the key users. The experimental results show that the proposed approach outperforms the support vector machine (SVM) method on a topic-independent corpus and can effectively reduce the false alarm rate.
关键词：sentiment analysis; violence risk; topic independent; semantic similarity; semantic rules sentiment analysis ; violence risk ; topic independent ; semantic similarity ; semantic rules