Aspect-Oriented Sentiment Analysis: A Topic Modeling-Powered Approach

Abstract

Because of exponential growth in the number of people who purchase products online, e-commerce organizations are vying for each other to offer innovative and improved services to its customers. Current platforms give its customers innovative services such as product recommendations based on their purchase histories and location, product comparison, and most importantly, a platform for expressing their experience and feedback. It is important for any e-commerce organization to analyze this feedback and to find out the sentiment of the customers for giving them better products and services. As large reviews may contain feedback in a mixed manner where a customer gives his opinion on different product features in the same review, finding out the exact sentiment is tedious. This work proposes aspect-specific sentiment analysis of product reviews using a well-sophisticated topic modeling algorithm called latent Dirichlet allocation (LDA). The topic words, thus, extracted are mapped with various aspects of an entity to perform the aspect-specific sentiment analysis on product reviews. Experiments with synthetic and real dataset show promising results compared to existing methods of sentiment analysis.

Keywords

• latent dirichlet allocation
• sentiment analysis
• topic modeling
• e-commerce
• aspect extraction
• text mining