Snippext: Semi-supervised Opinion Mining with Augmented Data
Table 9. Most frequent new opinion tuples discovered by Snippext.
['Tuples definitely recommend, hotel', 'Count 1411', 'Tuples own, bathroom', 'Count 211']
With these 4 models deployed, Snippext can extract 3.49M aspect-opinion tuples from the review corpus, compared to only 3.16M tuples extracted by the baseline pipeline. To better understand the coverage difference, we look into the aspect-opinion pairs extracted only by Snippext but not by the baseline pipeline. Observe that Snippext extracts more fine-grained opinions. For example, “hidden, fees” appears 198 times in Snippext’s extractions, out of 707 “fees” related extractions. In contrast, there are only 124 “fees” related extractions with the baseline method and the most frequent ones are “too many, fees”, which is less informative than “hidden fees” (and “hidden fees” are not extracted by the baseline method). As another example, there are only 95 baseline extractions about the price (i.e., contains “$” and a number) of an aspect. In comparison, Snippext extracts 21,738 (228× more) tuples about the price of an aspect (e.g., “$50, parking”).