Abstract:

Based on the shortcomings of sentiment analysis, this paper implemented a rule-based sentiment classification method and designed a basic feature set for machine learning methods. A sentiment analysis method via a combination of rule-based and machine learning methods is proposed. An effective integration feature set is obtained by adding various rule-based features to the basic feature set after expanding and converting them. The proposed method outperforms the baseline of any single method. Finally ensemble of three different classifiers is used to make further improvement on the performance of microblog sentiment classification.

Key words: microblog sentiment analysis, machine learning, rule-based method, feature embedding, system combination

摘要：

针对现有文本情感分析方法的不足, 设计了一种针对中文微博的基于词典的规则情感分类方法和用于机器学习方法的基本特征模板。提出一种机器学习与规则相融合的微博情感分类方法, 将用规则方法得到的多样化情感信息进行转化, 扩展并嵌入基本特征模板, 形成更有效的融合特征模板。通过 3 种分类模型集成, 提高微博情感分类的性能。

关键词: 微博情感分析, 机器学习, 规则方法, 特征嵌入, 系统融合