An approach to analyse and Forecast Social media data using Machine Learning and Data Analysis

Twitter is one of the most used social network platforms with more than 321 million active users, sending a daily average of 500 million Tweets. Twitter basically reaches a broad audience, connects many users, and also provides a useful point of view on an ongoing topic based on their interests. This research paper includes works of Machine Learning, NLP, and sentiment analysis on twitter data which is related to Goods and Service Tax (GST) collected from twitter by web scraping technology. Basically here we have found out how many people are against or for about the GST.Here we calculated the sentiments of positive, negative and neutral according to the twitter user. Here Data visualization also used to visualize the data can include cleaning checking transforming and finding the pattern. Machine learning and NLP are used on the data-driven model from the prediction purpose. Machine learning techniques applied to corresponding twitter dataset that contain reviews of GST users to find out the relevant information and inference. Machine learning used to extract the sentiments of the user and find future trends based on current uses.