Sentiment Analysis Dashboard for Socia Media comments using BERT

The automatic extraction of positive or negative attitude expressions from text, known as sentiment analysis, has drawn a lot of interest from academics in the last 10 years. They hold both favorable and unfavorable opinions on various individuals, groups, locations, occasions, and concepts. It is now feasible to start extracting feelings from social media, thanks to the tools given by NLP and machine learning, coupled with othermethods to work with massive quantities of text. In this work, we examine some of the difficulties in sentiment extraction, some of the methods used to overcome these difficulties, and our method. In this study, we explore the usage of Bidirectional Encoder Representations from Transformers (BERT) models for sentiment analysis on data generated on social media platforms like Twitter, YouTube, etc.