基于句法与主题扩展的中文微博情感倾向性分析模型

A Model for Sentiment Classification of Chinese Microblog Based on Parsing and Theme Extension

  • 摘要: 微博数据具有微博文本长度不一,文本内容主题发散性,夹杂微博专用符号等特性,需要一种融合句法分析、领域知识、表情符号等多因素的综合建模方法对社会、娱乐、安全等多领域微博进行情感分析. 文章提出了一种面向主题的中文微博情感建模方法,该模型涵盖了数据预处理、句法分析、主题扩展、领域知识、情感词上下文极性调整、表情符号等内容,最后以新浪微博采集数据,选取3个领域主题进行了实验,在特定的实验环境下,得到了较高的分析准确率.

     

    Abstract: The main features of microblog include different lengths, divergence of its themes, and inclusion of special symbols. It's essential for building a comprehensive modeling method, which integrating dependency sentence analysis, domain knowledge and emotions to analyze sentiment of microblog in various aspects, such as society, entertainment, security. In this paper, a topic-oriented sentiment analysis model for Chinese microblog was proposed, this model covers data preprocessing, dependency sentence analysis, theme extension, domain knowledge and rules, dynamic adjustment of polarity of sentiment words and emotions. Through the experiments of Sina weibo data from three different domains, this method obtains high analytical accuracy under specific experimental environment.

     

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