SARWAS Recommendation Framework Using Deep Learning

Abstract Sentiment-based recommendation systems are growing very fast nowadays , as users cannot express their opinion on the Likert scale from 1 to 5. Most of the current techniques work on either one of the parameters (ratings or reviews), mainly on reviews. This paper explored a new algorithm, SARWAS, involving both ratings and reviews for the recommendation system. This paper proposed a deep learning model using a sentiment and rating weighted association score (SARWAS) framework for combining ratings and reviews. We scraped reviews from e-commerce sites and calculated polarity and subjectivity for each review. Then a neural network model is further applied to calculate the weights and determine a combined score for a product. We evaluated the proposed model in terms of correlation between rating, review, and recommendation. It is being observed from the experiment that the proposed method produced satisfactory results in terms of accuracy. The correlation of reviews and recommendations is estimated to be more than ratings and recommendations with improved accuracy and precision of 95 percent and 89 percent.