Chinese Movie Comment Sentiment Analysis Based on HowNet and User Likes

Abstract

Emotional information in movie comments is critical to sentiment analysis, Sentiment analysis, which focuses on classify the comments into positive class and negative class according to sentiment lexicon, is one of the studies. Most of the existing researches are centered on sentiment words and user rating, while the user’s attitude towards comments are ignored. And, considering that Chinese is the second largest language in the world. In this paper, in order to get this point to be considered, we propose a method for Chinese movie comments sentiment analysis based on HowNet and user likes which we called HAL. Our research consists of four parts. First, we use HowNet sentiment lexicon to get a new lexicon in the field of movies. Second, we use the new lexicon and word segmentation tool named Jieba to segment the movie comments. Third, we use the user likes and sentiment words to get the positive feature and negative feature. Finally, we train the movie comment data using three models (SVC, LinearSVC, LogisticRegression). The experimental results show that our method performs better than HowNet-based method in Chinese movie comment sentiment analysis.