Detection of Abusive Bengali Comments for Mixed Social Media Data Using Machine Learning

Sultana, Sherin; Redoy, Md Omur Faruk; Nahian, Jabir Al; Masum, Abu Kaisar Mohammad; Abujar, Sheikh

doi:10.21203/rs.3.rs-2379359/v1

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Sultana et al (2023) employed Logistic Regression (LR), Multinomial Naive Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gradient Boosting (GB) for detecting abusive Bengali comments on social media. SVM achieved the best results, with an accuracy of 85.7% [13]. The lack of speci c information regarding the dataset used to train and evaluate the machine learning algorithms in the paper could have an impact on the results' external validity and reproducibility.…”

Section: Related Workmentioning

confidence: 99%

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Khalaf,

Yasmin,

Jisan

et al. 2024

Preprint

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Banglish slang is a distinctive mashup of Bengali and English terms that is widely used in messaging applications, social media, and other casual contexts. It has grown to be an essential component of Bangladesh's linguistic landscape. The use of Banglish slang in social media comments has become more common among young people from Bangladesh in recent years. Yet, because traditional language processing methods are not appropriate for managing such linguistic complexity, automated text analysis systems find it difficult to handle this informal communication style. Our proposed model captures the unique components of Banglish slang, such as slang phrases, misspellings, and abbreviations, by combining character-level and word-level properties since it is the most common form of communication and improves user interaction and content moderation. We remove special characters from the dataset and transform all test results to lowercase. After then, the stop words are removed from the dataset. Furthermore, we do tokenization using a pad sequence prior to classification. Stack Bidirectional Long Short-Term Memory (Bi-LSTM) model, a neural network architecture used in Natural Language Processing (NLP), is a state-of-the-art model utilized in this research to classify slang in Banglish words. On the training set with an outstanding 99% accuracy and a strong 79.8% validation accuracy, the suggested model beats traditional approaches. The Mendeley dataset was used to analyze social media comments to detect instances of Banglish slang. To carry out the work, approximately five thousand comments are considered overall. The experimental findings show how well our recommended approach works to identify Banglish slang. This method can be useful in monitoring content and language learning, among other real-world applications.

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Section: Related Workmentioning

confidence: 99%

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Khalaf,

Yasmin,

Jisan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Sultana et al [23] proposed six numerous machine learning models including logistic regression, multinomial naïve bayes, random forest, support vector machine, K-nearest neighbour and gradient boosting. For word embedding, TF-IDF transformer and TF-IDF vectorizer were used.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Cyberbullying Detection in Bangla Language

Nath,

Karim,

Miraz

2024

AETiC

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The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model’s accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.

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Hate speech detection in the Bengali language: a comprehensive survey

Al Maruf,

Abidin,

Haque

et al. 2024

J Big Data

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The detection of hate speech (HS) in online platforms has become extremely important for maintaining a safe and inclusive environment. While significant progress has been made in English-language HS detection, methods for detecting HS in other languages, such as Bengali, have not been explored much like English. In this survey, we outlined the key challenges specific to HS detection in Bengali, including the scarcity of labeled datasets, linguistic nuances, and contextual variations. We also examined different approaches and methodologies employed by researchers to address these challenges, including classical machine learning techniques, ensemble approaches, and more recent deep learning advancements. Furthermore, we explored the performance metrics used for evaluation, including the accuracy, precision, recall, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, specificity, and F1 score, providing insights into the effectiveness of the proposed models. Additionally, we identified the limitations and future directions of research in Bengali HS detection, highlighting the need for larger annotated datasets, cross-lingual transfer learning techniques, and the incorporation of contextual information to improve the detection accuracy. This survey provides a comprehensive overview of the current state-of-the-art HS detection methods used in Bengali text and serves as a valuable resource for researchers and practitioners interested in understanding the advancements, challenges, and opportunities in addressing HS in the Bengali language, ultimately assisting in the creation of reliable and effective online platform detection systems.

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Detection of Abusive Bengali Comments for Mixed Social Media Data Using Machine Learning

Cited by 4 publications

References 16 publications

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Detection of Banglish Slang in Social Media Comments Using a Hybrid Bidirectional Long Short-Term Memory (Bi-LSTM) Model

Deep Learning Based Cyberbullying Detection in Bangla Language

Hate speech detection in the Bengali language: a comprehensive survey

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