English(EN) Nobody Truly Agrees on Sentiment: Humans, Bespoke Tools, and LLMs Struggle with Social Media Texts

研究发现，人类和大型语言模型在社交媒体情感分析方面存在困难

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

一篇新发表在arXiv上的研究评估了情感分析工具和大型语言模型（LLMs）在处理社交媒体文本时的表现。研究发现，即使是人类标注员在分类情感时也只能达到中等程度的一致性，这凸显了任务固有的主观性。在评估的工具中，Twitter-roBERTa-base在二元情感分类方面与人类评分的匹配度最高，而Qwen3-32B、GPT-OSS-120B和Llama-4-Maverick-17B等大型语言模型则与人类表现出中等到实质性的一致性，并且它们自身之间也表现出高度一致性。 AI

影响强调了在可靠的社交媒体情感分析中，需要进行领域特定微调和以人为中心的评估。

排序理由评估大型语言模型和工具在特定任务上表现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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本文如何被排名

Signal score: 8 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 评估大型语言模型和工具在特定任务上表现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Himarsha R. Jayanetti, Sivakanesan Dhanushkanda, Shuai Hao, Michael L. Nelson, Michele C. Weigle · 2026-10-08 04:00

无人真正认同情感：人类、定制工具和LLM在处理社交媒体文本时均遇困难

arXiv:2610.10318v1 Announce Type: new Abstract: Social media is a rich source of real-time public sentiment, but widely used sentiment analysis tools are often applied without understanding their limitations. In this study, we evaluate the inter-rater reliability of three bespoke…

本文如何被排名

报道来源 [1]

无人真正认同情感：人类、定制工具和LLM在处理社交媒体文本时均遇困难

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