User Perspective Bangla Sentiment Analysis for Online Gaming Addiction using Machine Learning
6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022
; : 538-543, 2022.
Article
in English
| Scopus | ID: covidwho-2213194
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.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022
Year:
2022
Document Type:
Article
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