Multi-granular document-level sentiment topic analysis for online reviews

It is key to identify both sentiment and topic for well understanding and managing social media data such as online reviews and microblogs. This paper studies a robust and reliable solution for synchronous analysis of sentiment and topic in online reviews. Specifically, a probabilistic model is proposed for joint sentiment topic detection with multi-granular computation, named MgJST (multi-granular joint sentiment topic). The MgJST model introduces sentence level structural knowledge to detect sentiment and topic simultaneously from reviews based on latent Dirichlet allocation (LDA). The sets of experiments are conducted on seven sentiment analysis datasets. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art unsupervised approaches WSTM and STSM in terms of sentiment detection quality, and has powerful ability to extract topics from reviews.

Automated summary

This paper proposes a probabilistic model for joint sentiment topic detection in online reviews, which outperforms state-of-the-art unsupervised approaches in sentiment detection quality and topic extraction ability, as demonstrated through experiments on seven sentiment analysis datasets.