Aspect based opinion mining on restaurant reviews

Abstract

The way businesses are operating have changed due to the explosion of the internet. Social media has an increasing number of reviews as people are keen to express their opinions based on their experiences. Online reviews have become a precious asset in various disciplines such as intelligent marketing and decision-making.The number of reviews for a well-liked product might reach thousands. This makes it challenging for a prospective buyer to go through them and make up their minds. In order to overcome this challenge, a machine-learning system is needed. Aspect based Opinion mining can be used to extract the aspects from the reviews, then we can analyze the nature of the reviews and recommend them to all the customers. We plan to classify reviews about a target entity as positive, negative and neutral so that readers of the reviews do not have to go through all the reviews but instead can focus on functional items and applicable suggestions. This thesis is specifically focused on reviews in the domain of restaurants. This study extends our knowledge of online reviews by taking into account users’ wants and anticipating their future behavior. Several distinct evaluative linguistic nuances shed light on internet reviews. Using an assortment of models on generated benchmark datasets, we will also empirically show the efficacy of our strategy and show that the new techniques (or modified versions) are superior to, or at least on par with, state-of-the-art methods.