Enhancing User Feedback Analysis With Review Text Granularity For Better Sentiment And Rating Prediction
DOI:
https://doi.org/10.63665/6htp8683Keywords:
Sentiment Analysis, Natural Language Processing, Long Short-Term Memory (LSTM), Deep Learning, Rating Prediction, Opinion Mining, Recommendation SystemsAbstract
The exponential growth of user-generated content on digital platforms presents significant challenges in extracting meaningful insights from large-scale textual data. In particular, analyzing user sentiments and accurately predicting product ratings from online reviews require advanced natural language processing (NLP) techniques. This paper proposes a novel framework that leverages Long Short-Term Memory (LSTM) networks to capture contextual and sequential dependencies within review texts. Unlike conventional approaches that perform binary or categorical sentiment classification, the proposed model generates continuous and fine-grained sentiment scores, enabling a more nuanced understanding of user opinions. The integration of this sentiment representation with predictive modeling enhances the accuracy of rating prediction systems. Experimental results demonstrate that the proposed approach effectively captures complex linguistic patterns and dynamic emotional variations in textual data. The framework is applicable across multiple domains, including e-commerce, entertainment, and social media, and contributes to improving recommendation systems by enabling more personalized and context-aware user experiences.
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