A New Approach To Text Rating Classification Using Sentiment Analysis
This work addresses rating classification for product reviews, but it appears incremental as it builds on existing sentiment analysis methods.
The paper tackled the problem of classifying product reviews into higher or lower ratings by redefining sentiment proportions into a triangle structure to derive a new formula, proving a dependence between sentiments and ratings.
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.