Rule-Based Weibo Messages Sentiment Polarity Classification towards Given Topics

Abstract

Weibo messages sentiment polarity classification towards given topics refers to that the machine automatically classifies whether the weibo message is of positive, negative, or neutral sentiment towards the given topic. The algorithm the sentiment analysis system CUCsas adopts to perform this task includes three steps: (1) whether there is an “exp” (short for “expression having evaluation meaning”) in the weibo message; (2) whether there is a semantic orientation relationship between the exp and topic; (3) the sentiment polarity classification of the exp. CUCsas completes step (1) based on the sentiment lexicon and sentiment value assignment rules, completes step (2) based on the topic extraction and sentiment polarity classification rule base, and completes step (3) based on the sentiment computing rules. Taking 20 given topics and a total of 19,469 weibo messages released by SIGHAN-2015 Bake-off as the test data, the overall F value of the rule-based system CUCsas is 0.69 in the unrestricted test. 1 Algorithm Description The locutionary subjectivity denotes the locutionary agent’s self-expression of cognition, feeling or perception in the use of language (John Lyons, 1995). And the evaluation is one type of locutionary subjectivity. An evaluation discourse consists of four basic elements: E(s) = {sub, obj, exp, com}. Herein, “E(s)” represents an evaluation discourse, and “sub”, “obj”, “exp” and “com” refers to the subject of evaluation, the object of evaluation, an expression having evaluation meaning, and other discourse components respectively (Zhou Hongzhao et al., 2014). The study of this paper is under the condition of knowing obj (= the given topic) in the weibo message, enabling the system automatically recognize whether there is an exp in the same weibo message. If there is not, the system will output result [topic: 0]; if there is, the system will make a further identification that whether there is a semantic orientation relationship between the exp and the given topic. If there is not, the system will outputs result [topic 0]; if there is, the system will further classify the sentiment polarity of the exp. If it is positive, the system will output result [topic 1]; if it is negative, the system will output result [topic -1]; if it is neutral, the system will output result [topic 0]. Apparently, the algorithm is different from some widely used machine learning sentiment polarity classification algorithms, such as Naive Bayes, Max Entropy, Boosted Trees and Random Forest (Amit Gupte et al., 2014). Figure 1 shows the algorithm of the the system of rule-based weibo messages sentiment polarity classification towards given topics. Example (1) :三星发布 Galaxy S6 和 S6 Edge,下月正式开卖。 (There is no exp in the weibo message. → Output: 0)