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New Nepali Sentiment Benchmark NEPSEE Released

By PulseAugur Editorial · [1 sources] ·

Researchers have developed NEPSEE, a new benchmark for Nepali sentiment analysis, which utilizes double annotation to ensure label consistency. The benchmark consists of 3,189 sentences from Nepali public figures' tweets, with labels agreed upon by two annotators. In evaluations, Claude Opus 5.5 demonstrated superior performance on a five-point sentiment scale, outperforming encoder models by a significant margin. Additionally, an audit of tokenizers revealed that default Nepali BERT models often mishandle Devanagari script, impacting performance. AI

IMPACT This benchmark could improve sentiment analysis capabilities for Nepali language models and highlight issues with tokenizer handling of specific scripts.

RANK_REASON The cluster contains an academic paper detailing a new benchmark dataset and evaluation results. [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 Nepali Sentiment Benchmark NEPSEE Released

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The cluster contains an academic paper detailing a new benchmark dataset and evaluation results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Gaurab Baral, Mimansak Nepal, Grishma Basnet ·

    NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis

    arXiv:2610.19190v1 Announce Type: new Abstract: Many sentiment datasets use one annotator for each item, so the test labels show the conventions of that person. We build a Nepali benchmark from two annotators who labelled the same sentences blind. They labelled 5,614 sentences fr…

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