用生成模型辅助判断新闻后何时发声最有效
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification
- 用文本生成模型驱动分类器，优化意见表达时机判断
- 在专业分析师数据上验证，准确率显著提升
- 适合关注舆情分析与决策支持的研究者
文本生成模型的发展使我们能按需生成连贯、有说服力的内容。然而现实中，人们不会持续输出文字或表达观点——消费者会在权衡产品优劣后撰写评论，专业分析师则在重大新闻发布后出具报告。本质上，观点表达通常由特定事件或信号触发。尽管观点挖掘研究长期发展，但合适的表达时机仍鲜被关注。为此，本研究提出一项新任务：识别新闻触发的观点表达时机。研究基于专业股票分析师的行为构建新数据集，并提出一种以决策为导向的方法，利用文本生成模型引导分类模型，从而提升整体性能。实验表明，生成内容从多角度提供新洞察，有效助力识别最佳表达时机。
原文摘要 · Abstract (English)
The advancement of text generation models has granted us the capability to produce coherent and convincing text on demand. Yet, in real-life circumstances, individuals do not continuously generate text or voice their opinions. For instance, consumers pen product reviews after weighing the merits and demerits of a product, and professional analysts issue reports following significant news releases. In essence, opinion expression is typically prompted by particular reasons or signals. Despite long-standing developments in opinion mining, the appropriate timing for expressing an opinion remains largely unexplored. To address this deficit, our study introduces an innovative task - the identification of news-triggered opinion expressing timing. We ground this task in the actions of professional stock analysts and develop a novel dataset for investigation. Our approach is decision-focused, leveraging text generation models to steer the classification model, thus enhancing overall performance. Our experimental findings demonstrate that the text generated by our model contributes fresh insights from various angles, effectively aiding in identifying the optimal timing for opinion expression.
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