关键词: 情感分析, 主题模型, 循环神经网络

Abstract: Almost all state-of-art methods for sentiment analysis can hardly avoid extracting sentiment features and applying them to classifiers for detecting. However, with the characteristics of diversity expressions and scattered themes of network texts, it’s too difficult to extract more suitable and proper sentiment features. This paper proposes a novel algorithm to solve such problems. Firstly, original texts need to be clustered by topics with LDA (latent Dirichlet allocation) model. Then, for each topic dataset, language models are trained for positive and negative samples by using recurrent neural network. Finally, two kinds of probabilities of topic and sentiment are combined for evaluating text sentiment polarity. Through this method, this paper firstly standardizes text expression by dividing subcategories, limiting changes of words meaning under different topics, and then utilizes language model to avoid the difficulty of extracting features, making it possible to be internalized in the process of training model. The experimental results on IMDB show that the proposed method improves a lot in terms of accuracy with topic clustering.

Key words: sentiment analysis, topic model, recurrent neural network