SC192014
Sentiment Detection on Food Items in Restaurant Reviews

Restaurant goers always refer to restaurant reviews website before deciding to visit a restaurant. However, those websites do not recommend the food of a restaurant based on customer reviews. Reading a lot of customer reviews is not viable as it is too time-consuming and taxing. Customers should be informed of the sentiment of food items given by previous customers that act as food recommendations to make better decisions. In this project, a web application that automatically analyses the sentiments expressed towards food items from online customer reviews will be developed. The sentiment data will then be visualized to help restaurant-goers obtain relevant recommendations of food items. To achieve this, the system will be able to extract restaurant reviews from existing restaurant review sites, perform food name extraction using NLP, analyze the sentiment of food items using machine learning, and visualize the result. The web application will display food recommendations when restaurant-goers view a restaurant on the restaurant review website. The system also has an analytics web application for detailed sentiment analysis for restaurant owners to aid them in making better business planning and for restaurant-goers to obtain more information on customer reviews for the food items recommended by the system. The system is expected to help restaurant-goers make better decisions, increase restaurant goers’ efficiencies, and improve customer engagements.

Name: Chong Onn Keat

Matrics No: 132894

Email: chongonnkeat@student.usm.my

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Supervisor: Dr. Jasy Liew Suet Yan

Email: jasyliew@usm.my