A Comprehensive Pre-processing Approach for High-Performance Classification of Twitter Data with several Machine Learning Algorithms

Producing an average of five hundred million tweets per date, Twitter has grown as one of the most comprehensive platforms of data interpretation for the researchers. Beforehand, various researches have been conveyed on twitter data i.e., sentimental analysis. Nevertheless, not much research has been performed to classify the tweets in terms of categories so that tweets can be spread as per user preferences. In this research, we started by constructing four comprehensive classes: politics, sports, crime and natural. Next, we implemented our proposed preprocessing model on the raw twitter dataset. After that, we implemented different machine learning techniques (Random Forest, K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree and Support Vector Machine) to classify the twitter data. Finally, we examined…