Towards Quantifying the Distance between Opinions
Table 5: We compare the quality of variants of Opinion Distance measures on opinion clustering task with ARI.
['[EMPTY]', 'Difference Function', 'Seanad Abolition', 'Video Games', 'Pornography']
distribuThe results of different variants are shown in Table 5. [CONTINUE] OD significantly outperforms OD-parse: We observe that compared to OD-parse, OD is much more accurate. On the three datasets, OD achieves an average weighted F1 score of 0.54, 0.56 and 0.41 respectively compared to the scores of 0.01, -0.01 and 0.07 by OD-parse. [CONTINUE] Table 5 shows that OD is significantly better than Jensen-Shannon divergence on all the three datasets. [CONTINUE] Sentiment polarity shifters have a high impact on clustering performance of opinion distance: We find that not utilizing the sentiment polarity shifters, especially in case of datasets "Video games" and "Pornography" hurts the Opinion Representation phase, and thereby leads to incorrect computation of opinion distance. This is evident from the significant drop in ARI score from OD to OD (no polarity shifters) since the only change in those variants is of sentiment polarity shifters.