量化意见之间的距离
Towards Quantifying the Distance between Opinions
摘要：
公共政策、治理和商业战略中的关键决策越来越依赖于对组成成员（例如公民、股东）的需求和意见的深入了解。虽然收集关于某个主题的大量意见变得越来越容易，但有必要使用自动化工具来帮助浏览意见空间。在这种情况下，理解和量化观点之间的相似性是关键。我们发现，仅仅基于文本相似性或整体情绪的措施往往无法有效地捕捉意见之间的距离。因此，我们提出了一种新的距离度量来捕获意见之间的相似性，该距离度量利用了细致入微的观察--类似的意见在特定的相关感兴趣实体上表达了类似的情感极性。具体来说，在无监督的环境中，与现有方法相比，我们的距离度量实现了更好的调整后兰德指数得分（高达56倍）和轮廓系数（高达21倍）。类似地，在监督设置中，与依赖于文本相似性、立场相似性和情感相似性的现有方法相比，我们的意见距离度量实现了更好的准确性（高达20%的增加
英文摘要：
Increasingly, critical decisions in public policy, governance, and business strategy rely on a deeper understanding of the needs and opinions of constituent members (e.g. citizens, shareholders). While it has become easier to collect a large number of opinions on a topic, there is a necessity for automated tools to help navigate the space of opinions. In such contexts understanding and quantifying the similarity between opinions is key. We find that measures based solely on text similarity or on overall sentiment often fail to effectively capture the distance between opinions. Thus, we propose a new distance measure for capturing the similarity between opinions that leverages the nuanced observation -- similar opinions express similar sentiment polarity on specific relevant entities-of-interest. Specifically, in an unsupervised setting, our distance measure achieves significantly better Adjusted Rand Index scores (up to 56x) and Silhouette coefficients (up to 21x) compared to existing approaches. Similarly, in a supervised setting, our opinion distance measure achieves considerably better accuracy (up to 20% increase) compared to extant approaches that rely on text similarity, stance similarity, and sentiment similarity
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
