一种基于LDA主题模型的评论文本情感分类方法

针对互联网出现的评论文本情感分析，引入潜在狄利克雷分布(Latent Dirichlet allocation,LDA)模型，提出一种分类方法。该分类方法结合情感词典，依据指定的情感单元搭配模式，提取情感信息，包括情感词和上、下文。使用主题模型发掘情感信息中的关键特征，并融入到情感向量空间中。最后利用机器学习分类算法，实现中文评论文本的情感分类。实验结果表明，提出的方法有效降低了特征向量的维度，并且在文本情感分类上有很好的效果。

Method of Sentiment Analysis for Comment Texts Based on LDA

A method of sentiment analysis for online comment texts is proposd based on the latent Dirichlet allocation (LDA) model. The method extracts the sentiment information containing sentiment words and context with the sentiment word dictionary according to the specified collocation patterns of sentiment unit. Use the LDA model to mine the key features of the sentiment information and then combine them into the sentiment vector space. The machine-learning algorithm is used to classify the sentiment polarity of Chinese comment texts. After experiment, the presented method is proved to be effective in reducing dimensionality and text sentiment classification.