A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA

With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and providers of the products and services. Therefore, sentiment analysis and opinion mining have become important research areas. In user reviews mining, topic modeling based approaches and Latent Dirichlet Allocation (LDA) are significant methods that are used in extracting product aspects in aspect based sentiment analysis. However, LDA cannot be directly applied on user reviews and on other short texts because of data sparsity problem and lack of co-occurrence patterns. Several studies have been published for the adaptation of LDA for short texts. In this study, a novel method for aspect based sentiment analysis, Sentence Segment LDA (SS-LDA) is proposed. SS-LDA is a novel adaptation of LDA algorithm for product aspect extraction. The experimental results reveal that SS-LDA is quite competitive in extracting products aspects.

Automated summary

With the widespread use of social networks and other platforms, the volume of user-generated textual data is growing rapidly, making sentiment analysis and opinion mining in user reviews more and more important. To tackle issues like data sparsity and lack of co-occurrence patterns, studies have proposed methods like SS-LDA to adapt LDA for short texts. Experimental results indicate that SS-LDA performs competitively in extracting product aspects.