Text Mining Customer Reviews For Aspect-based Restaurant Rating

TL;DR

This paper presents a text mining approach to automatically analyze customer reviews and assign aspect-based restaurant ratings, providing insights into strengths and weaknesses through sentiment analysis and visualizations.

Contribution

It introduces a novel system that combines NLP techniques and sentiment analysis to generate detailed, aspect-specific restaurant ratings from user reviews.

Findings

Effective extraction of aspect-based sentiment scores

Generation of visual word clouds for review insights

Improved restaurant rating accuracy with more feedback

Abstract

This study applies text mining to analyze customer reviews and automatically assign a collective restaurant star rating based on five predetermined aspects: ambiance, cost, food, hygiene, and service. The application provides a web and mobile crowd sourcing platform where users share dining experiences and get insights about the strengths and weaknesses of a restaurant through user contributed feedback. Text reviews are tokenized into sentences. Noun-adjective pairs are extracted from each sentence using Stanford Core NLP library and are associated to aspects based on the bag of associated words fed into the system. The sentiment weight of the adjectives is determined through AFINN library. An overall restaurant star rating is computed based on the individual aspect rating. Further, a word cloud is generated to provide visual display of the most frequently occurring terms in the reviews. The more feedbacks are added the more reflective the sentiment score to the restaurants' performance.