ENHANCING USER FEEDBACK ANALYSIS WITH REVIEW TEXT GRANULARITY FOR BETTER SENTIMENT AND RATING PREDICTION

Abstract

In the era of digital technology, the vast amount of usergenerated content on online platforms poses significant challenges for analyzing large volumes of text to understand user emotions and predict product ratings. The increasing prevalence of online reviews necessitates advanced natural language processing (NLP) techniques to effectively extract meaningful insights from textual data. This paper introduces a novel approach leveraging Long Short-Term Memory (LSTM) networks combined with sophisticated NLP methods to capture the nuanced emotional expressions within review texts. Unlike traditional binary sentiment classification, the proposed model provides a continuous and fine-grained sentiment scoring that reflects the intensity and subtlety of user opinions, thereby enhancing the sentiment analysis process. The use of LSTM enables the model to effectively capture sequential dependencies and contextual relationships in text data, improving the understanding of complex linguistic patterns within reviews. This comprehensive sentiment representation is integrated with predictive modelling techniques to enhance the accuracy of rating predictions in recommendation systems. The proposed framework demonstrates the capability to harness rich textual information and dynamic sentiment variations, making it highly applicable across multiple domains such as entertainment, e-commerce, and social media. This approach not only improves prediction outcomes but also supports more personalized and meaningful user experiences.