摘要
情感分析作为文本挖掘的一个新型领域,可用于分类、归纳用户发布的产品评论,从而有助于商家改善服务,提高产品质量;同时为其他消费者提供购买决策。本文提出一种基于情感词抽取与LDA特征表示的情感分析方法,对产品评论进行褒贬二元分类。在情感词抽取中,采用人工构造的情感词典对预处理之后的文本抽取情感词;用LDA模型建立文档的主题分布,以评论-主题分布作为特征,用SVM分类器进行分类。实验结果表明,本文方法在评论褒贬分类方面有着良好的效果。
As a new field of sentiment analysis, text mining can be used to classify andsummarize online users＇ product reviews. The summaries and classification help provider to improve service and product quality, and also provide buyer advicse for other consumers. The paper proposes a sentiment analysis method based on sentiment words extraction and LDA feature representation, for online products＇ reviews making binary classification. The processing steps are as follows： extract sentiment words from the preprocessed text using the manually created sentiment dictionary; create the topic subject distribution of documents using the LDA model; take comment-subject distribution as feature; do classification based on the SVM classifier. Experiments show that, the proposed method has excellent effects of review of judgments classification.
出处
《计算机与现代化》
2014年第5期79-83,共5页
Computer and Modernization
关键词
情感分析
情感词抽取
主题模型
sentiment analysis
sentiment words extraction
latent Dirichlet allocation
topic model
support vector machine
