论文标题
量化意见之间的距离
Towards Quantifying the Distance between Opinions
论文作者
论文摘要
越来越多的公共政策，治理和商业战略中的关键决定依赖于对组成成员的需求和意见（例如公民，股东）的更深入了解。虽然对一个主题收集大量意见变得更加容易，但是自动化工具有必要帮助浏览意见空间。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，仅基于文本相似性或总体情绪的措施通常无法有效地捕获意见之间的距离。因此，我们提出了一种新的距离措施，以捕获利用细微差别观察的观点之间的相似性 - 相似的观点对特定的相关实体表达了相似的情感极性。具体而言，与现有方法相比，在无监督的环境中，我们的距离度量可以明显更好地调整后的兰德指数分数（高达56倍）和轮廓系数（高达21倍）。同样，与依靠文本相似性，立场相似性和情感相似性的现存方法相比
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
