@inproceedings{kawser-etal-2025-hybrid,
title = "A Hybrid Transformer{--}Sequential Model for Depression Detection in {B}angla{--}{E}nglish Code-Mixed Text",
author = "Kawser, Md Siddikul Imam and
Al Abrar, Jidan and
Kabir, Mehebub Bin and
Chowdhury, Md. Rayhan and
Bahari, Md Ataullah",
editor = "Alam, Firoj and
Kar, Sudipta and
Chowdhury, Shammur Absar and
Hassan, Naeemul and
Prince, Enamul Hoque and
Tasnim, Mohiuddin and
Rony, Md Rashad Al Hasan and
Rahman, Md Tahmid Rahman",
booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.banglalp-1.8/",
pages = "107--112",
ISBN = "979-8-89176-314-2",
abstract = "Depression detection from social media text is critical for early mental health intervention, yet existing NLP systems underperform in low-resource, code-mixed settings. Bangla-English code-mixing, common across South Asian online communities, poses unique challenges due to irregular grammar, transliteration, and scarce labeled data. To address this gap, we introduce DepressiveText, a 7,019-sample dataset of Bangla-English social media posts annotated for depressive signals, with strong inter-annotator agreement ($\kappa = 0.84$). We further propose a hybrid architecture that combines BanglishBERT embeddings with an LSTM classifier, enabling the model to capture both contextual and sequential cues. Comparative experiments with traditional ML, deep learning, and multilingual transformer baselines demonstrate that our approach achieves the highest performance, with an accuracy of 0.8889. We also employ LIME to enhance interpretability by identifying key lexical triggers. Our findings underscore the effectiveness of hybrid transformer{--}sequence models for low-resource code-mixed NLP and highlight their potential in real-world mental health applications."
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<abstract>Depression detection from social media text is critical for early mental health intervention, yet existing NLP systems underperform in low-resource, code-mixed settings. Bangla-English code-mixing, common across South Asian online communities, poses unique challenges due to irregular grammar, transliteration, and scarce labeled data. To address this gap, we introduce DepressiveText, a 7,019-sample dataset of Bangla-English social media posts annotated for depressive signals, with strong inter-annotator agreement (ąppa = 0.84). We further propose a hybrid architecture that combines BanglishBERT embeddings with an LSTM classifier, enabling the model to capture both contextual and sequential cues. Comparative experiments with traditional ML, deep learning, and multilingual transformer baselines demonstrate that our approach achieves the highest performance, with an accuracy of 0.8889. We also employ LIME to enhance interpretability by identifying key lexical triggers. Our findings underscore the effectiveness of hybrid transformer–sequence models for low-resource code-mixed NLP and highlight their potential in real-world mental health applications.</abstract>
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%0 Conference Proceedings
%T A Hybrid Transformer–Sequential Model for Depression Detection in Bangla–English Code-Mixed Text
%A Kawser, Md Siddikul Imam
%A Al Abrar, Jidan
%A Kabir, Mehebub Bin
%A Chowdhury, Md. Rayhan
%A Bahari, Md Ataullah
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Hassan, Naeemul
%Y Prince, Enamul Hoque
%Y Tasnim, Mohiuddin
%Y Rony, Md Rashad Al Hasan
%Y Rahman, Md Tahmid Rahman
%S Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-314-2
%F kawser-etal-2025-hybrid
%X Depression detection from social media text is critical for early mental health intervention, yet existing NLP systems underperform in low-resource, code-mixed settings. Bangla-English code-mixing, common across South Asian online communities, poses unique challenges due to irregular grammar, transliteration, and scarce labeled data. To address this gap, we introduce DepressiveText, a 7,019-sample dataset of Bangla-English social media posts annotated for depressive signals, with strong inter-annotator agreement (ąppa = 0.84). We further propose a hybrid architecture that combines BanglishBERT embeddings with an LSTM classifier, enabling the model to capture both contextual and sequential cues. Comparative experiments with traditional ML, deep learning, and multilingual transformer baselines demonstrate that our approach achieves the highest performance, with an accuracy of 0.8889. We also employ LIME to enhance interpretability by identifying key lexical triggers. Our findings underscore the effectiveness of hybrid transformer–sequence models for low-resource code-mixed NLP and highlight their potential in real-world mental health applications.
%U https://aclanthology.org/2025.banglalp-1.8/
%P 107-112
Markdown (Informal)
[A Hybrid Transformer–Sequential Model for Depression Detection in Bangla–English Code-Mixed Text](https://aclanthology.org/2025.banglalp-1.8/) (Kawser et al., BanglaLP 2025)
ACL