Jointly Discovering Fine-grained and Coarse-grained Sentiments via Topic Modeling

The ever-increasing user-generated contents in social media and other web services make it highly desirable to discover opinions of users on all kinds of topics. Motivated by the assumption that individual word and paragraph in documents will deliver fine-grained (e.g., "laudatory", "annoyed" or "boring") and coarse-grained (e.g., positive, negative or neutral) sentiments about certain topics respectively, this paper focuses on a deeper thematic level to jointly disentangle fine-grained and coarse-grained opinions towards topics in terms of sentiment analysis, named as LDA with multi-grained sentiments (MgS-LDA). As a result, the proposed MgS-LDA not only discovers the topics in social media, but also identifies opinions about a given topic in terms of fine-grained and coarse-grained sentiment. Results of several experiments show that our proposed MgS-LDA achieves better performance on both sentimental classification and topic modeling than related methods.