Iteration-based naive Bayes sentiment classification of microblog multimedia posts considering emoticon attributes

Abstract

Microblog (such as Weibo) is an integrated social platform of vital importance in the internet age. Because of its diversity, subjectivity and timeliness, microblog is popular among public. In order to perform sentiment classification on microblog posts and overcome the limitation of text information, a fine-grained sentiment analysis method is proposed, in which emoticon attributes are considered. Firstly, the microblog texts are pre-processed to remove some stop words and noise information such as links. Then the data is matched in the sentiment lexicon, and when the first matching succeeds, the second matching is performed in the emoticon dictionary. The emoticons in the emoticon dictionary are transformed into vector form. Through these matching, the emotional features are vectorized and other text features are considered. Finally, the iterative-based naive Bayesian classification method is used for sentiment classification. The experiment results show that emoticons have obvious effect on facilitating the sentiment classification of microblog posts, and the proposed sentiment classification method achieved better than average results in term of classification accuracy compared with state-of-art techniques.