主题情感混合模型可以同时提取语料的主题信息和情感倾向。针对短文本特征稀疏的问题,主题情感联合分析方法较少的问题,该文提出了BJSTM模型(Biterm Joint Sentiment Topic Model),在BTM模型(Biterm Topic Model)的基础上,增加情感层的设置,从而形成“情感-主题-词汇”的三层贝叶斯模型。对每个双词的情感和主题进行采样,从而对整个语料的词共现关系建模,一定程度上克服了短文本的稀疏性。实验表明,BJSTM模型在无监督情感分类和主题提取方面都有不错的表现。

The joint topic and sentiment model is aimed at efficiently detecting topics and emotions for the given corpus. Faced with the sparsity of short texts and the lack of sentiment/topic analysis methods, this paper proposes a novel way called Biterm Joint Sentiment Topic Model (BJSTM). A sentiment layer is added to Biterm Topic Model, thus a three-layer Bayesian model of “sentiment-topic-term” is formed. By sampling the sentiment and topic of each biterm, BJSTM could depict the word co-occurrence of the whole corpus and overcome the sparsity of short texts to some extent. The experimental results show that BJSTM gets better performance in sentiment classification as well as topic extraction.