A New Approach To Text Rating Classification Using Sentiment Analysis

TL;DR

This paper introduces a novel method for classifying product review ratings by transforming sentiment probabilities into a triangle structure, revealing a dependence between sentiment proportions and ratings.

Contribution

It proposes a new formula based on sentiment proportions structured as a triangle to improve text rating classification, a novel approach in sentiment analysis.

Findings

Dependence between sentiments and ratings established

New formula for rating classification derived

Triangle structure for sentiment proportions introduced

Abstract

Typical use cases of sentiment analysis usually revolve around assessing the probability of a text belonging to a certain sentiment and deriving insight concerning it; little work has been done to explore further use cases derived using those probabilities in the context of rating. In this paper, we redefine the sentiment proportion values as building blocks for a triangle structure, allowing us to derive variables for a new formula for classifying text given in the form of product reviews into a group of higher and a group of lower ratings and prove a dependence exists between the sentiments and the ratings.