Sentimental Analysis and Visualization of Food Reviews from Zomato using Tableau

Abstract

In today's digital era where everybody has their own opinion and so can post various feedbacks regarding different products. Reviews are critical in nature and can affect the market value of a product. Amazon, Zomato, Quora, Twitter, etc . are few such platforms that provide space for product reviews. Since reviews are textual and diversified; a collective representation will help users to summarize their opinions. Honest food reviews and its analysis is still a challenge. Sentiment Analysis or opinion mining indicates the utilization of computational linguistics, textual analysis, biostatistics, and NLP (Natural Language Processing) to consistently recognize, skin, compute and grasp effective levels and abstract information. In this paper, we used the database of 'Zomato Pune' downloaded from Kaggle which is used as a basic dataset to summarize the food reviews by customers into 3 basic categories whether positive, negative or neutral based on the food being served at the restaurants. A visual analytical tool is being used linked with Tableau to give clear and interactive charts: Bar Chart, Word-Cloud, and Packed Bubble as visualization techniques. Charts will help users to compare multiple options interactively at the same time.