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.