An Empirical Analysis of theRole of Amplifiers, Downtoners, and Negationsin Emotion Classification in Microblogs
TABLE IV: Comparison of modifier detection methods on modEval corpus. The results of the best method for each modifier and the average are highlighted in boldface for precision, recall, and F1, respectively.
['Next-2 Modifier', 'Next-2 P', 'Next-2 R', 'DepTree F1', 'DepTree P', 'DepTree R', 'SVM F1', 'SVM P', 'SVM R', 'F1']
The simplest method, the next-2 heuristic, shows best results throughout all modifier types. The performance for downtoners is substantially lower than for negations and amplifiers. (80 % F1 on product reviews). The main source of errors for the DepTree approach are errors in the dependency trees because of missing punctuation. The source of errors for the comparably low performance on downtoner scope detection depends on the method. For the next-2 approach, a challenge is that downtoner cues appear more often after the scope. For the DepTree approach, we observe that downtoner cues are more often not a direct child of associated emotion words. Regarding the SVM approach, we presume two main reasons for the limited performance: Firstly, we prioritize negations and amplifiers and secondly, because we have a limited training set for downtoner.