Sentiment Analysis on Food Reviews Using Data Visualization Tools

Abstract

Scientists are delving deeply into different types of analysis of which sentiment analysis is the prominent one, which in the current times has emerged as a remarkable field of study. Positive, negative, and neutral sentiment analysis are the three types of emotional analysis. Users may share good and negative evaluations, which can be desirable or undesirable depending on the user experience, on the internet, and on e-websites such as Twitter and Facebook. Our research will look at the most popular points of view to determine the scope and kind of sentiment analysis based on Food Reviews. The study's major goal was to discover food-related sentiments among social media users. We want to show how to gather, pre-process, and categorize data from various social media platforms, and how to analyse attitudes based on Food Reviews. We collected Food Review-related data from Amazon Fine Food Reviews, an information-sharing social media network, to investigate public opinions regarding food review emotions. Various tools are employed in our research work such as matplotlib, pandas, word cloud and NLTK package. After conducting the research, it was found that the majority population had positive sentiments related to the food products and in many cases, they revealed negative sentiments as well.