关键词: 主题模型, LDA, 情感, 评论挖掘, 主题-对立情感挖掘模型（TOSM）

Abstract: At mining product topics and opposite sentiment information of the topics from online reviews, in order to help manufactures and service providers improve their products and services, and help customers make decisions, this paper proposes a topic model called topic-opposite sentiment mining model (TOSM) based on latent Dirichlet allocation (LDA), which assumes that all words in a single sentence are generated from one topic and one sentiment. This paper extends LDA to TOSM with adding the sentiment layer, so that TOSM can detect topics and opposite sentiment of topic simultaneously from reviews. Moreover, this paper uses sentiment lexicon in TOSM to make the opposite sentiment represented clearly. This paper does three experiments with the reviews of electronic devices from Amazon and the reviews of restaurants from Yelp. The experimental results show that the topic-sentiment found by TOSM matches evaluative details of the reviews, and TOSM outperforms other generative models.

Key words: topic model, latent Dirichlet allocation (LDA), sentiment, review mining, topic-opposite sentiment mining model (TOSM)