COMPARATIVE EVALUATION OF MACHINE LEARNING CLASSIFIERS FOR CYBERBULLYING DETECTION
Volume: 13 - Issue: 06 - Date: 29-06-2024
Approved ISSN: 2278-1412
Published Id: IJAECESTU409 | Page No.: 139-145
Author: Nidhi koyale
Co- Author: Dr. Pushparaj Singh Chauhan
Abstract:- - In this paper evaluated the performance of several machine learning classifiers in detecting cyberbullying. The
models tested include BaggingClassifier, SGDClassifier, LogisticRegression, DecisionTreeClassifier,
RandomForestClassifier, LinearSVC, AdaBoostClassifier, MultinomialNB, and KNeighborsClassifier. The evaluation
metrics considered were Accuracy, Precision, Recall, and F1 Score. Among the tested models, the BaggingClassifier
emerged as the top performer with an accuracy of 0.928, a precision of 0.964, a recall of 0.925, and an F1 score of 0.944,
indicating its high effectiveness and balance between precision and recall. The SGDClassifier followed closely, achieving
an accuracy of 0.927, a precision of 0.958, a recall of 0.930, and an F1 score of 0.944, demonstrating excellent
performance as well. The LogisticRegression model also showed strong results with an accuracy of 0.926, a precision of
0.964, a recall of 0.922, and an F1 score of 0.943. DecisionTreeClassifier and RandomForestClassifier achieved slightly
lower accuracies of 0.923 and 0.919, respectively, but maintained strong precision and recall. LinearSVC had an
accuracy of 0.917, while AdaBoostClassifier, MultinomialNB, and KNeighborsClassifier showed lower accuracies of
0.908, 0.893, and 0.858, respectively, indicating their relative ineffectiveness for this task. The results suggest that
BaggingClassifier and SGDClassifier are highly reliable choices for cyberbullying detection, with LogisticRegression
also being a strong contender
Key Words:-Cyberbullying Detection, Machine Learning Classifiers, Comparative Evaluation, Cyberbullying Analysis, Classification Algorithms, Text Analysis, Social Media Monitoring
Area:-Engineering
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