An Empirical Analysis of theRole of Amplifiers, Downtoners, and Negationsin Emotion Classification in Microblogs
TABLE III: Modifier scope detection corpora.
['Modifier', 'modEval', 'trainNeg', 'trainAmp', 'trainDown']
Therefore, more specifically, we use three different sampling methods to obtain a corpus densily populated with relevant instances, but not limited to those detected with our resources: Equally-sized subsets are sampled based on the occurrence of (1) both modifier cue and emotion word, (2) only modifier cue, (3) only emotion word. Using different sampling methods enables us to expand our emotion and modifier lexicons with emotion-bearing words and modifier cues found during annotation. We annotate 1,000 tweets resulting in 1,913 modifier-emotion word pairs. : The corpus modEval contains one-third of the annotations from each modifier type. We use this corpus to evaluate the performance of the different modifier scope detection approaches. Furthermore, we create three corpora trainNeg, trainAmp and trainDown containing the remaining two thirds of annotations for scope detection model training. The table also shows that of all detected modifiers, downtoners are the least common ones.