Unsupervised Aspect Extraction Algorithm for opinion mining using topic modeling

Abstract

With the massive use of electronic gadgets and the developing fame of web-based media, a great deal of text information is being produced at the rate never observed. It is not feasible for people to pursue all information produced and discover what is being investigated in their area of interest. To determine topics in large textual documents Topic modeling is used. Topic Modeling Algorithms are Unsupervised Machine Learning approaches which are widely used and have proven to be successful in the area of Aspect-based Opinion Mining to extract ‘latent’ topics, which are aspects of interest. In this paper, the approaches that are widely used for topic modeling are examined and compared to find their importance in detecting topics based on metrics such as Perplexity and Coherence. As a result, Latent Dirichlet Allocation is a good topic modeling algorithm compared to Latent Semantic Analysis and Hierarchical Dirichlet Process for aspect extraction process in aspect-based opinion mining. Also, we have proposed an unsupervised aspect extraction algorithm based on topic models for Aspect-based Opinion mining.