Mining Worse and Better Opinions
Table 7: Amazon dataset - parameters: equation (5), bins = 3.
['[BOLD] Category C [ITALIC] i', '[BOLD] minRev=20 #I [ITALIC] j', '[BOLD] minRev=20  [ITALIC] #Rev', '[BOLD] minRev=20  [ITALIC] (%)', '[BOLD] minRev=50 #I [ITALIC] j', '[BOLD] minRev=50  [ITALIC] #Rev', '[BOLD] minRev=50  [ITALIC] (%)', '[BOLD] minRev=100 #I [ITALIC] j', '[BOLD] minRev=100  [ITALIC] #Rev', '[BOLD] minRev=100  [ITALIC] (%)']
For each category Ci, we apply the methodology three times, modifying the minimum number of reviews (minRev) for each item Ij, in order to discard items with few reviews. We set Kbins=3 Nevertheless, in all cases (but Oral Irrigators), the percentage values increase when minRev increase (for Magnifiers, it remains the same with minRev=50,100).