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Cited by 60Hate Speech and Cyberbullying DetectionText and Document Classification TechnologiesSentiment Analysis and Opinion Mining

Deployment of Machine Learning and Deep Learning Algorithms in Detecting Cyberbullying in Bangla and Romanized Bangla text: A Comparative Study

Ahmed, Md. Tofael, Rahman, Maqsudur, Nur, Shafayet, Islam, Azm, Das, Dipankar·2021·2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)
DOI 10.1109/icaect49130.2021.9392608
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Abstract

Popularity of social media has increased rapidly and now it is very easy to interact with different persons across social media. As a result, cyberbullying towards people across social media has also increased. As cyberbullying can cause significant emotional and psychological distress, it should be detected and prevented. A lot of work has been done by researchers for cyberbullying detection in several languages except Bangla and Romanized Bangla. Therefore, we developed a model to detect cyberbullying from Bangla and Romanized Bangla texts using Machine Learning and Deep Learning algorithms. We also presented a comparative analysis among the algorithms in terms of accuracy, precision, recall, f1score and roc area. We prepared three datasets for Bangla, Romanized Bangla and combination of both from social media. The three datasets contained 5000 Bangla, 7000 Romanized Bangla and a combination of 12000 Bangla and Romanized Bangla texts. The preprocessed datasets were trained using the Machine Learning and Deep Learning algorithms and the model performances were evaluated to present the comparative analysis. Deep Learning algorithm CNN performed best for the Bangla Dataset by achieving 84% accuracy. For the other two datasets Machine Learning algorithm Multinomial Naïve Bayes performed best by achieving 84% accuracy in Romanized Bangla dataset and 80% accuracy in the combined dataset.

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  • Ahmed et al. [18] discovered that for one dataset, CNN outperforms other ML and DL models, whereas ML models outperform DL models for the other two datasets.

    “In their experiment, they discovered that for one dataset, CNN, a DL algorithm outperforms other ML and DL models, whereas ML models outperform DL models for the other two datasets.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [18]

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Authors
Ahmed, M. T., Rahman, M., Nur, S., Islam, A., & Das, D.
Affiliations
  • Comilla University (BD)
  • Port City International University (BD)
  • University of Rajshahi (BD)
ORCID
Venue
2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)
Publisher
IEEE
Published
2021-02-19
Pages
1-10
DOI
10.1109/icaect49130.2021.9392608
OpenAlex
W3153771145
Cited by
60
References
34
Citations by year
2026: 3 · 2025: 11 · 2024: 19 · 2023: 14 · 2022: 12 · 2021: 1
FWCI
5.31
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
cyberbullying detection, Bangla text, social media, multilingual text classification, convolutional neural networks, multinomial naive Bayes, algorithm comparison, Bengali language

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