Towards Quantifying the Distance between Opinions
This addresses the need for better tools to navigate opinion spaces in domains like governance and strategy, though it is incremental as it builds on existing sentiment and similarity approaches.
The paper tackled the problem of quantifying similarity between opinions for automated analysis in public policy and business, proposing a new distance measure based on sentiment polarity on specific entities, which achieved up to 56x better Adjusted Rand Index scores and up to 20% higher accuracy compared to existing methods.
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