Enhancing Sentiment Analysis and Rating Prediction Using the Review Text Granularity (RTG) Model

Abstract

In the era of digital technology, when material created by users is prevalent on online platforms, considerable difficulty is faced in analyzing large volumes of text in order to comprehend user emotions and forecast product ratings. The rapid rise in online reviews and comments necessitates the use of advanced tools to assess this data and extract valuable insights. This is considered crucial for the effectiveness of recommendation systems in many industries. This paper introduces the Review Text Granularity (RTG) Model, a new way to use the complex information in review texts to improve sentiment analysis and rating prediction. The RTG Model uses an advanced approach to scoring sentiments. It measures the strength of sentiments and gives a continuous sentiment score instead of a simple positive or negative label. This makes it different from other binary sentiment analysis methods. Multiple predictive modeling techniques are used, which makes it possible for this comprehensive sentiment analysis to greatly improve the accuracy of rating predictions. It has been shown by research that the depth of textual reviews is better captured and measured by the RTG Model, providing a more detailed and precise picture of user opinions. The RTG Model works really well, making rating predictions in recommendation systems more accurate and useful. A detailed study using a real-world dataset of IMDb movie reviews demonstrated this. The study emphasizes the benefits of utilizing intricate sentiment scores in addition to conventional rating data. The potential applicability of the RTG Model in several fields, including entertainment, e-commerce, and social media, is demonstrated, leading to enhanced and tailored user experiences.