A two-stage unsupervised sentiment analysis method

In this paper, the SASC (Sentiment Analysis based on Sentiment Clustering) method is proposed to solve the problems of low accuracy and poor stability in the review sentiment clustering methods. Through two-stage sentiment clustering, the hidden sentiment information among the review texts is obtained to improve the accuracy and stability of the results. Specifically, in the first stage, the review representation vector construction method is put forward through the topic model LDA. Then the second stage uses K-means algorithm to achieve further optimization of the sentiment clustering results. In the experiment part, the evaluation methods of sentiment clustering are firstly introduced, and then a series of experiments are carried out on two widely used datasets Large Movie Review Dataset v1.0 and Multi-Domain Sentiment Dataset. Experiment results indicate that compared with other methods, the SASC method proposed in this paper has better clustering accuracy and stability.