arXiv Computation and Language By Ayon Roy, Sadman Sharif, Syed Shams Islam

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

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BanglishDepYT is a native Bengali depression detection dataset comprising 191,000 unlabeled YouTube comments and 1,000 bilingual‑annotated samples. The authors introduce Emotion‑Preserving Hybrid preprocessing (EPH), which retains emoticons, Banglish tokens, negations, and emotional elongation, improving SVM F1 from 0.7062 to 0.8570. Domain‑adaptive pretraining of SahajBERT on in‑domain comments yields the best performance (F1 = 0.8161, MCC = 0.6272), and LLM‑assisted annotation enables pseudo‑labeling that further boosts AUC from 0.8499 to 0.8731.

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arXiv Computation and Language
Oct 15

NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis

NEPSEE is a Nepali sentiment analysis benchmark created by having two annotators independently label 5,614 tweets from Nepali public figures on a five‑point scale. Only 3,189 sentences with identical polarity are kept for a three‑class version, while 766 sentences with adjacent labels are used in a five‑point task. Baseline results show NepBERTa achieving a macro‑F1 of 0.877 on the three‑class benchmark, with commercial LLMs performing similarly; on the five‑point task, Claude Opus 5.5 outperforms encoders by 0.10–0.12 macro‑F1, and a tokenizer audit reveals that restoring Devanagari vowel signs can significantly alter performance.

By Gaurab Baral, Mimansak Nepal, Grishma Basnet