Mining Worse and Better Opinions
Table 8: Booking dataset considering different languages - parameters: equation (5), bins = 3. Not enough Italian reviews were available for Los Angeles and Sydney.
['[BOLD] Category C [ITALIC] i', '[BOLD] English #I [ITALIC] j', '[BOLD] English  [ITALIC] #Rev', '[BOLD] English  [ITALIC] (%)', '[BOLD] Italian #I [ITALIC] j', '[BOLD] Italian  [ITALIC] #Rev', '[BOLD] Italian  [ITALIC] (%)', '[BOLD] French #I [ITALIC] j', '[BOLD] French  [ITALIC] #Rev', '[BOLD] French  [ITALIC] (%)']
For the Booking dataset, we straight consider only hotels with at least 100 reviews. We perform three experiments according to the languages of reviews (English, Italian, and French). For each experiment, Kbins=3 and we report the number of items (#I) The percentage of items for which the average score of the last bin is higher than the average score of the first bin is above 90% in all the cases.