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.
Highlights
Customer feedbacks are useful for firms in order for them to recognize its strengths and weaknesses, and generate ideas to improve its services
Zomato and Yelp are some of the many available crowdsourcing applications that gather customer feedback on restaurants
A model proposed by [10] called Multi-Aspect Sentiment (MAS) model to discover topics in customer reviews and extract fragments of text that correspond to rateable aspect to support numerical ratings
Summary
Customer feedbacks are useful for firms in order for them to recognize its strengths and weaknesses, and generate ideas to improve its services. The application of text mining for analysis of customer textual reviews to quantify it through star rating based on predetermined decision factors prove to be beneficial to help cope with the information overload and facilitate decision making. While there has been significant amount of study on text mining and sentiment analysis to understand customer reviews, converting textual data to numeric assessment to reflect overall perception of customer has not been extensively explored. This study aims to develop a mobile and web application that serves as a platform for diners to write feedback on dining experience. The system uses these reviews as corpus to determine customer perception on the restaurant in general and on the specific aspects such as ambience, cost, food, hygiene and service. A word cloud of the customers’ general sentiment will give the restaurant the visual illustration of top qualities and issues
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