Topic-Specific Emotion Mining Model for Online Comments

Nowadays, massive texts are generated on the web, which contain a variety of viewpoints, attitudes, and emotions for products and services. Subjective information mining of online comments is vital for enterprises to improve their products or services and for consumers to make purchase decisions. Various effective methods, the mainstream one of which is the topic model, have been put forward to solve this problem. Although most of topic models can mine the topic-level emotion of the product comments, they do not consider interword relations and the number of topics determined adaptively, which leads to poor comprehensibility, high time requirement, and low accuracy. To solve the above problems, this paper proposes an unsupervised Topic-Specific Emotion Mining Model (TSEM), which adds corresponding relationship between aspect words and opinion words to express comments as a bag of aspect–opinion pairs. On one hand, the rich semantic information obtained by adding interword relationship can enhance the comprehensibility of results. On the other hand, text dimensions reduced by adding relationships can cut the computation time. In addition, the number of topics in our model is adaptively determined by calculating perplexity to improve the emotion accuracy of the topic level. Our experiments using Taobao commodity comments achieve better results than baseline models in terms of accuracy, computation time, and comprehensibility. Therefore, our proposed model can be effectively applied to online comment emotion mining tasks. View Full-Tex