A Sentiment Score and a Rating Based Numeric Analysis Recommendations System: A Review

Online purchases have been significantly increasing in the web world. Some of the big giants who have dominated the E-Commerce market worldwide are Amazon, FlipKart, Walmart and many others. Data generation has increased exponentially and analysis of this kind of dynamic data poses a major challenge. Further, facilitating consumer satisfaction by recommending the right product is another main challenge. This involves a significant number of factors like review ratings, normalization, early rating, sentiment computations of a sentence consisting of conjunctions, categorizing the sentiment score as positive, negative and neutral score for a given product review. Finally, the product which has the highest positive and least negative score must be suggested for the end user. In this paper, we discuss the work done under rating based numerical analysis methods which considers the transactions done by the end user. In the second part of the paper, we present an overview of sentiment score computed and its significance in improving the efficiency of recommendation systems. The main objective of this review is to understand and analyze different methods used to improve the efficiency of the current recommendation systems, thereby enhancing the credibility of product recommendations.