Antu Chowdhury


2024

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CUETSentimentSillies@DravidianLangTech-EACL2024: Transformer-based Approach for Sentiment Analysis in Tamil and Tulu Code-Mixed Texts
Zannatul Tripty | Md. Nafis | Antu Chowdhury | Jawad Hossain | Shawly Ahsan | Avishek Das | Mohammed Moshiul Hoque
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

Sentiment analysis (SA) on social media reviews has become a challenging research agenda in recent years due to the exponential growth of textual content. Although several effective solutions are available for SA in high-resourced languages, it is considered a critical problem for low-resourced languages. This work introduces an automatic system for analyzing sentiment in Tamil and Tulu code-mixed languages. Several ML (DT, RF, MNB), DL (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLM-RoBERTa, m-BERT) are investigated for SA tasks using Tamil and Tulu code-mixed textual data. Experimental outcomes reveal that the transformer-based models XLM-R and m-BERT surpassed others in performance for Tamil and Tulu, respectively. The proposed XLM-R and m-BERT models attained macro F1-scores of 0.258 (Tamil) and 0.468 (Tulu) on test datasets, securing the 2nd and 5th positions, respectively, in the shared task.

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CUETSentimentSillies@DravidianLangTech EACL2024: Transformer-based Approach for Detecting and Categorizing Fake News in Malayalam Language
Zannatul Tripty | Md. Nafis | Antu Chowdhury | Jawad Hossain | Shawly Ahsan | Mohammed Moshiul Hoque
Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages

Fake news misleads people and may lead to real-world miscommunication and injury. Removing misinformation encourages critical thinking, democracy, and the prevention of hatred, fear, and misunderstanding. Identifying and removing fake news and developing a detection system is essential for reliable, accurate, and clear information. Therefore, a shared task was organized to detect fake news in Malayalam. This paper presents a system developed for the shared task of detecting and classifying fake news in Malayalam. The approach involves a combination of machine learning models (LR, DT, RF, MNB), deep learning models (CNN, BiLSTM, CNN+BiLSTM), and transformer-based models (Indic-BERT, XLMR, Malayalam-BERT, m-BERT) for both subtasks. The experimental results demonstrate that transformer-based models, specifically m- BERT and Malayalam-BERT, outperformed others. The m-BERT model achieved superior performance in subtask 1 with macro F1-scores of 0.84, and Malayalam-BERT outperformed the other models in subtask 2 with macro F1- scores of 0.496, securing us the 5th and 2nd positions in subtask 1 and subtask 2, respectively.