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Text Mining Customer Reviews for Aspect-Based Restaurant Rating


Affiliations
1 University of San Jose-Recoletos, Cebu City, Philippines
 

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.

Keywords

Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-Based Scoring.
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Abstract Views: 361

PDF Views: 164




  • Text Mining Customer Reviews for Aspect-Based Restaurant Rating

Abstract Views: 361  |  PDF Views: 164

Authors

Jovelyn C. Cuizon
University of San Jose-Recoletos, Cebu City, Philippines
Jesserine Lopez
University of San Jose-Recoletos, Cebu City, Philippines
Danica Rose Jones
University of San Jose-Recoletos, Cebu City, Philippines

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.

Keywords


Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-Based Scoring.

References