Data analytics to reflect the current politics on twitter by using Machine Learning

Many unstructured text messages, chats, postings, and blogs have been produced as social media users have skyrocketed. In addition to serving as a platform for information dissemination, social media platforms provide a natural forum for the wide dissemination of ideas and viewpoints that acquire traction when supported by a sizable audience. The positive or negative feelings individuals have about a person, group, or location might be reflected in how well they are known. Vast quantities of material on social media platforms like Twitter provide political insights that may mine to understand public sentiment and foretell election outcomes. An effort is made in this paper to mine tweets for political sentiment and represent this extraction process as a supervised learning problem. Twitter users’ attitudes toward the main national political parties campaigning in the 2019 Indian election are analyzed, and tweets related to the upcoming election are extracted. The sentiment analysis categorization algorithm is thus ready to forecast the tweets’ propensity to infer election outcomes. The classification model is built using Long Short Term Memory (LSTM) and compared to traditional ML approaches.