Tweets Categorization and Comparison of Results using Machine Learning Models

Abstract

Various social media platforms are used on the daily basis to spread trending news and topics. Among those, twitter is widely used all over the world by the people to put forward and spread about their opinions. This creates a very powerful impact on various topics and people do this to show their support or contradiction. On considering analysis of sentiments using twitter, five main approaches are used which include Logistic Regression, Decision Tree, and XGBOOST which comes under Machine Learning and TF-IDF and Bag of Words which comes under Natural Language Processing. Initially data pre-processing happens, followed by visualization of words, then comes feature extraction, and last step is generating comparison of various Machine Learning models.