  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
