Food Quality Prediction Based on Reviews

Abstract

Nowadays with the advancement of the internet and technologies people prefer to provide reviews for almost every kind of thing and put them online. It is vital to bring out information from the huge amount of accessible text reviews. This is why consumer’s feedback is important. People of almost every age often visit restaurants. In today’s world food review is the fundamental requirement for visiting restaurants. But selecting a restaurant based on reviews is not quite an easy task. Deciding whether a food is worth having or not can be difficult. Several factors including the price, quality, taste, quantity can influence the actual worth of a food. From the perspective of a consumer, it is a dilemma to select a food appropriately. Food quality prediction can be a challenging task due to the high number of reviews that should be considered for the accurate prediction. People are keen to find out whether a food is worth having or not before visiting a restaurant. Most people nowadays select restaurants based on their preferred food’s review. But the reviews present on the social platforms are mostly broad. People don’t find it useful to read the whole review. Therefore, a model which is capable of accepting reviews as input and is able to predict the food quality as output can become a great solution to this problem. During my research, I have proposed a technique to predict consumer feedback from the online reviews given for a food by using Deep Learning, Artificial Neural Network and Long Short-Term Memory algorithms. Based on those reviews, the customers will be able to find out the most suited restaurant for their preferred food. This will also help the restaurant owners to improve their food quality based on their customer's review. The purpose of this study is to represent a different view than what has already been done to solve this problem.