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New dataset and methods improve depression detection in Bengali social media

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BanglishDepYT, a new dataset and methodology for detecting depression in native Bengali social media comments. The approach, EPH (Emotion-Preserving Hybrid preprocessing), effectively handles informal language elements like emoticons and romanized Bengali words, significantly improving detection accuracy compared to standard preprocessing methods. Further domain-adaptive pretraining of SahajBERT on unlabeled comments achieved state-of-the-art results, outperforming larger multilingual models. The study also leveraged Qwen2.5-7B for LLM-assisted annotation, demonstrating a scalable path for expanding labeled datasets. AI

IMPACT This research offers a novel approach to handling informal language in low-resource settings, potentially improving mental health support tools for Bengali speakers.

RANK_REASON This is a research paper detailing a new dataset and methodology for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and methods improve depression detection in Bengali social media

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This is a research paper detailing a new dataset and methodology for a specific NLP task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ayon Roy, Sadman Sharif, Syed Shams Islam ·

    BanglishDepYT: Depression Detection in Native Bengali Social Media with Emotion-Preserving Preprocessing and Domain-Adaptive Pretraining

    arXiv:2611.01633v1 Announce Type: new Abstract: Depression affects millions of Bengali speakers across South Asia, yet automated detection systems have relied almost entirely on datasets translated from English. Such datasets cannot capture the emoticons, romanized Bengali words,…

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