Sentimental Analysis on Zomato Restaurant Reviews using Bi-LSTM

Sentiment analysis is a vital aspect of understanding public opinion and sentiment towards products and services. This paper presents a sentiment analysis work focused on Zomato restaurant reviews in Bangalore, aiming to classify restaurants into positive, negative, or neutral sentiment categories based on customer reviews. Bi-LSTM and Bi-GRU models are employed to capture contextual information in the sequential data of reviews. Additionally, sentiment analysis techniques, including Word2Vec, VADER, and Sentiment Intensity Analyzer, are integrated to enhance the sentiment classification process. Through rigorous experimentation, the performance of these models and techniques is evaluated. The proposed models demonstrate promising accuracy rates in sentiment classification. By enhancing and expanding the sentiment analysis framework, this paper contributes to a deeper understanding of public sentiment towards Zomato restaurants in Bangalore. The insights derived from this study can facilitate informed decision-making for both restaurant owners and customers, ultimately improving the dining experience and customer satisfaction with accuracy of 98.6%.