## 1 Introduction  
With the rapid development of Web 2.0, more and more people retrieve and share information on social media. Microblog, as a popular user-generated content service, is attracting more and more users. Unlike conventional web text, its length is limited to 140 characters. This feature heightens user engagement in publishing their opinions more frequently and quickly. Microblogging has grown into a rich corpus of users' emotions and opinions, especially on hot topics. Consequently, sentiment analysis of microblog text has attracted much attention in recent years and has spread from computer science to management sciences and social sciences due to its importance in business and society [1]. There is an increasing interest in sentiment analysis of microblogs in several areas, such as predicting consumer reviews [2], market trends [3] and specific events [4]. Most of the works provided in the literature depend on specific training data. They usually perform well only when there is a good match between the training and test data. In order to train an accurate domain-specific sentiment classifier, a large number of labeled samples are needed, which are costly and time-consuming to obtain [5]. Our previous work illustrated a semantic rule-based topic-independent sentiment analysis approach [6]. This paper is an expanded edition of [6] and combines the approach in [6] with background checking to detect violent Chinese microblogs.  
Violence risk assessment of microblogs has been rarely reported in the literature. This is contradictory to the observation that more and more terrorism speeches and violence threats are published on the Internet every day. This phenomenon can be attributed to several reasons: (1) there is no definitive annotated corpus, which is important for machine learning-based studies; (2) most violent microblogs are insincere or a form of emotional catharsis, which hinder such studies; (3) the violence risk assessment of microblogs involves multiple topics, which is not well-addressed by the existing topic-dependent methods. In recent years, the topic-independent textual analysis has attracted increasing attention. For English corpuses, [PERSON] et al. [7] introduced a method to determine topic-independent bias scores for words. [PERSON] and [PERSON] [8] proposed weakly supervised techniques to reduce the dependency on training data. The topic-independent sentiment analysis for Chinese text is still rarely reported, probably because the characteristics of Chinese text make it more difficult to analyze: (1) A Chinese microblog contains more information than English within the same length limit, while the syntax and semantics are more diverse; (2) There are usually several subsentences contained in one message, and they may not necessarily have coherent sentiment [9]. That is to say, it is very likely to have two opposite emotions within one microblog [10]; (3) Unlike English, a Chinese sentence has no spaces between words. Each Chinese sentence should be first split into a sequence of words before further analysis. Therefore, the methods of analysis of English corpuses cannot be directly applied to Chinese text.  
This paper aims to detect violent Chinese microblogs with a lower false alarm rate. Hence, the approach should not depend on topic-specific training data. The contributions of this paper are summarized as follows:  
1. A framework for step by step evaluation of the violence risk of Chinese microblogs is proposed.
2. Sentimental analysis is combined with background checks for violence activity detection.
3. Our method is topic-independent and easy to apply.  
This paper is arranged as follows: Section 2 describes related works. Section 3 describes our approach for the calculation of polarity of sentiment and estimation of risk. Experimental design and validation are included in Section 4. Section 5 concludes this paper.