用论据分析提升投资观点推荐精准度
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis
- 结合价格差异与论据分析评分投资观点
- 可识别高盈利潜力的投资意见
- 适合关注投资决策与风险行为的研究者
在互联网和社交媒体快速发展的时代，人们在线分享观点的频率极高，海量信息使得全面分析变得不切实际。为此，亟需高效的推荐系统来筛选并呈现重要且相关的观点。本文提出一种双路径论据挖掘方法，兼顾专业与普通投资者视角。第一种策略利用目标价与收盘价之间的差值作为观点指标；第二种策略运用论据挖掘原理对投资者观点进行打分，并据此排序。实验结果表明，该方法能有效识别具有更高盈利潜力的观点。此外，研究还拓展至风险分析，探讨推荐观点与投资者行为之间的关系，为采纳这些观点后的潜在结果提供全面视角。
原文摘要 · Abstract (English)
In the era of rapid Internet and social media platform development, individuals readily share their viewpoints online. The overwhelming quantity of these posts renders comprehensive analysis impractical. This necessitates an efficient recommendation system to filter and present significant, relevant opinions. Our research introduces a dual-pronged argument mining technique to improve recommendation system effectiveness, considering both professional and amateur investor perspectives. Our first strategy involves using the discrepancy between target and closing prices as an opinion indicator. The second strategy applies argument mining principles to score investors' opinions, subsequently ranking them by these scores. Experimental results confirm the effectiveness of our approach, demonstrating its ability to identify opinions with higher profit potential. Beyond profitability, our research extends to risk analysis, examining the relationship between recommended opinions and investor behaviors. This offers a holistic view of potential outcomes following the adoption of these recommended opinions.
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