Aspect Based Sentiment Analysis for Restaurant Recommendation System

Abstract

"This study aims to analyse Sri Lankan restaurant reviews to pinpoint key characteristics highlighted by users. By extracting these features, sentiment scores can be assigned to evaluate user preferences for specific aspects of the product or service. While many recommendation systems rely on English datasets, this research focuses on enhancing recommendation accuracy by incorporating insights from English reviews. Extracting features from English restaurant reviews and providing polarity scores for both the overall product/service and the specific features identified enables users to gain a comprehensive understanding of the offerings, thus improving recommendation accuracy. The proposed methodology uses SVM (Support Vector Machine) and GBR (Gradient Boosting Regressor) ensemble methods to determine review polarity through contextual analysis. This study will explore how developing an ensemble approach can enhance the overall accuracy of sentiment classification. A dataset of restaurant reviews from TripAdvisor and Kaggle is used to assess the method. The results demonstrate that these models function effectively in NLP applications, advancing the field of Aspect-Based Sentiment Analysis, with trials showing the proposed methodology achieving a high accuracy rate of 93% in correctly identifying the sentiments of restaurant reviews."