A New Approach To Text Rating Classification Using Sentiment Analysis

In our current day and age, reviews are part of almost every product/service provided on the internet[14], as seen in [8] it is the primary way for a company to get an understanding concerning the amount of success their product has and as examined in [7] for the customer to build trust in purchasing or using a service of which only a description or a picture exits. Therefore, a need for a deeper understanding and analysis of those reviews are needed[9] for any individual who wishes to derive various consequences regarding a product. Standard methods for such insight derivation include sentiment analysis, around which we will formulate a new approach for review rating classification. Reviews across the internet mainly consist of text-based and rating-based formats, where in many cases, a combination of both is considered a single review; the method developed in this paper focuses on the ability to associate a review to a rating cluster based on sentiment proportions. We will define two main groups: one group consisting of a majority of reviews higher than three stars (in a 5-star ranking system) and another group of all reviews, which correspond to the less than three stars.