Towards Quantifying the Distance between Opinions
Table 4: The quality of opinion distance when leveraged as a feature for multi-class classification. Each entry in + X feature should be treated independently. The second best result is italicized and underlined.
['Baselines', 'Seanad Abolition', 'Video Games', 'Pornography']
For completeness, here we also compare against unigram or n-gram based classifiers [CONTINUE] The classification performance of the baselines is reported in Table 4. [CONTINUE] SVM with only OD features outperforms many baselines: We see that on "Video Games" and "Pornography" datasets, the classification performance based on SVM with only OD is significantly better than the SVM with any other combination of features excluding OD. For the "Seanad Abolition" dataset, there is one exception: SVM with unigrams and LSA features performs slightly better than OD. [CONTINUE] nating word "democracy". SVM with OD and baseline features further improves classification performance: We see that SVM with OD and bigrams achieves the best multi-class classification performance on the "Seanad Abolition" dataset. On the "Pornography" dataset, we observe SVM with OD + LSA to improve classification performance by nearly 2%.