关键词: 主题模型, LDA, 情感分析, 混合模型

Abstract: Supervised and semi-supervised sentiment classification methods need label corpora for classifier training. To solve this problem, an unsupervised topic and sentiment unification model (UTSU model) is proposed based on the LDA model. UTSU model imposes a constraint that all words in a sentence are generated from one sentiment and each word is generated from one topic. This constraint conforms to the sentiment expression of language and will not limit the topic relation of words. UTSU model is compeletly unsupervised and it needs neither labeled corpora nor sentiment seed words. The experiments of sentiment classification show that UTSU model comes close to supervised classification methods and outperforms other topic and sentiment unification models. UTSU model improves the F1 value of sentiment classification 2% than ASUM model and 16% than JST model.

Key words: topic model, latent Dirichlet allocation (LDA), sentiment analysis, unification model