An Unsupervised Method for Short-Text Sentiment Analysis Based on Analysis of Massive Data

• 作者: Qiong Liu Zhenhua Huang Zhenrong Zhao Zhenyu Wang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: SENTIMENT ANALYSIS SHORT text EMOTIONAL WORDS MASSIVE data
• 年，卷（期）: 2015,（1）
• 页码范围: 49-50
• 页数: 2页
• 分类号: C5

摘要

Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses singlecharacter and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.