Abstract
This paper provides a comprehensive system for sentiment analysis and rate prediction of hotel reviews, combining modern natural language processing techniques with machine learning algorithms. The major purpose is to automatically assess consumer input and forecast related scores based on textual reviews. The system employs regression models to predict star ratings from the textual data and sentiment analysis to categorise reviews into good, negative, or neutral categories. The dataset consists of a large corpus of hotel reviews collected from various online platforms. Experimental results demonstrate the effectiveness of the proposed approach, with high accuracy in sentiment classification and rating prediction. The findings indicate that automated sentiment analysis and rating prediction can provide valuable insights for hotel management, helping to enhance customer satisfaction and improve sequence.
Published Version
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