Integrating Sentiment Analysis With Machine Learning for Cyberbullying Detection on Social Media
The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape which poses detrimental effects on individuals and broader societal frameworks. A viable approach to addressing this pervasive issue lies in the accurate identification of cyberbullying within social media a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10955379/ |
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| Summary: | The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape which poses detrimental effects on individuals and broader societal frameworks. A viable approach to addressing this pervasive issue lies in the accurate identification of cyberbullying within social media as it constitutes a substantial portion of digital interactions. State-of-the-art solutions predominantly rely on pre-trained language models and machine learning algorithms; however, these methods are often associated with substantial computational overheads and the development of advanced cyberbullying detection algorithms remains limited. This paper presents a unique framework that integrates sentiment analysis with machine learning algorithms to enhance the detection of cyberbullying on social media. Sentiment analysis explores the linguistic and emotional characteristics of cyberbullying messages which helps the framework to identify key sentiment indicators that differentiate harmful interactions from benign ones. Furthermore, we provide the most optimal text preprocessing steps which are ordered in a way that improves text quality for cyberbullying detection. These steps ensure high-quality input data for machine learning models which significantly enhances their performance. Additionally, we tackle the challenge of penta-class imbalanced data by incorporating resampling within the framework without causing bias. We employ several machine learning algorithms within the framework, the Extra Tree classifier achieved the best results with an accuracy of 95.38% and F1 score of 0.95. The results demonstrate that the integration of sentiment analysis significantly improves classification accuracy compared to conventional methods for the task of cyberbullying detection. |
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| ISSN: | 2169-3536 |