ABSTRACT
Sentiment analysis is the modern Natural Language Processing (NLP) technique for determining the sentiment of a user. The recent COVID-19 pandemic has pushed people of all ages, particularly the youth to get directly or indirectly involved in internet activities, one of which is online gaming. People have become increasingly involved in online gaming since they have easy access to the internet via smartphones. This research study has attempted to investigate online gaming addiction using different machine learning classification algorithms from over 401 data points. People of all ages, particularly students in high school, college, and university, are considered for data collection. After preprocessing and feature engineering the collected data, six state-of-the-art machine learning classification algorithms viz. Decision Tree, Random Forest, Multinomial Naive Bayes, Extreme Gradient Boosting, Support Vector Machine and K Nearest Neighbor are used to train the model. All six classifiers predict with high accuracy, with Multinomial Naive Bayes (MNB) having the highest accuracy of 73.27%. © 2022 IEEE.