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TEXT MINING CUSTOMER REVIEWS FOR ASPECTBASED RESTAURANT RATING

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Author :  Jovelyn C. Cuizon , Jesserine Lopez and Danica Rose Jones

Affiliation :  University of San Jose-Recoletos

Country :  Cebu Philippines

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 6, December, 2018

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.

Keyword :  Text Mining, Sentiment Analysis, Natural Language Processing, Aspect-based scoring

Journal/ Proceedings Name :  International Journal of Computer Science & Information Technology (IJCSIT)

URL :  http://aircconline.com/ijcsit/V10N6/10618ijcsit05.pdf

User Name : jack
Posted 07-02-2019 on 16:33:57 AEDT



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