发现并验证了网络情绪表达中的重复拉长现象对情感分析的关键作用
The Overlooked Repetitive Lengthening Form in Sentiment Analysis
- 构建首个聚焦重复拉长形式的多领域数据集Lengthening，含85万样本
- 实证表明重复拉长句可作文档级情感标志，显著提升分析效果
- 提出ExpInstruct框架，用少量数据让开源模型媲美GPT-4的性能与解释力
在线交流中，人们常以非正式风格（如表情包、表情符号）表达观点。尽管语言模型在非正式文本处理上已有广泛研究，但一种独特且强调性的表达方式——重复拉长形式（RLF）长期被忽视。本文探讨两个核心问题：1）RLF是否对情感分析（SA）重要？2）大语言模型能否理解RLF？受先前语言学研究启发，我们构建了首个专注于RLF的情感分析多领域数据集Lengthening，包含85万条样本。此外，提出可解释指令微调框架ExpInstruct，通过两阶段训练提升大模型在RLF上的表现与可解释性。还提出一种统一方法量化模型对非正式表达的理解能力。结果表明，RLF具有强表达力，可作为文档级情感的特征标志，具备在线内容分析潜力。微调后的预训练语言模型（PLMs）性能超越零样本GPT-4，但在解释性上仍不及。最终，ExpInstruct可在有限样本下使开源模型达到零样本GPT-4在性能和解释性上的水平。代码与样例数据见https://github.com/Tom-Owl/OverlookedRLF。
原文摘要 · Abstract (English)
Individuals engaging in online communication frequently express personal opinions with informal styles (e.g., memes and emojis). While Language Models (LMs) with informal communications have been widely discussed, a unique and emphatic style, the Repetitive Lengthening Form (RLF), has been overlooked for years. In this paper, we explore answers to two research questions: 1) Is RLF important for sentiment analysis (SA)? 2) Can LMs understand RLF? Inspired by previous linguistic research, we curate \textbf{Lengthening}, the first multi-domain dataset with 850k samples focused on RLF for SA. Moreover, we introduce \textbf{Exp}lainable \textbf{Instruct}ion Tuning (\textbf{ExpInstruct}), a two-stage instruction tuning framework aimed to improve both performance and explainability of LLMs for RLF. We further propose a novel unified approach to quantify LMs' understanding of informal expressions. We show that RLF sentences are expressive expressions and can serve as signatures of document-level sentiment. Additionally, RLF has potential value for online content analysis. Our results show that fine-tuned Pre-trained Language Models (PLMs) can surpass zero-shot GPT-4 in performance but not in explanation for RLF. Finally, we show ExpInstruct can improve the open-sourced LLMs to match zero-shot GPT-4 in performance and explainability for RLF with limited samples. Code and sample data are available at https://github.com/Tom-Owl/OverlookedRLF
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