Towards Quantifying the Distance between Opinions

  • Saket Gurukar The Ohio State University
  • Deepak Ajwani University College Dublin
  • Sourav Dutta Huawei Ireland Research Center
  • Juho Lauri Nokia Bell Labs
  • Srinivasan Parthasarathy The Ohio State University
  • Alessandra Sala Nokia Bell Labs


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.

How to Cite
Gurukar, S., Ajwani, D., Dutta, S., Lauri, J., Parthasarathy, S., & Sala, A. (2020). Towards Quantifying the Distance between Opinions. Proceedings of the International AAAI Conference on Web and Social Media, 14(1), 229-239. Retrieved from