An Empirical Analysis of theRole of Amplifiers, Downtoners, and Negationsin Emotion Classification in Microblogs
TABLE II: Emotion classification corpora.
['emotion', 'trainRepr', 'testRepr', 'train']
The hashtags are #glad, #happiness, #happy, #joy, #lucky, #luck, and #pleasure for joy, #anger, #hate, #hatred, and #rage for anger, #afraid, #angst, #fear, #panic, #scare, and #worry for fear, #bitter, #grief, #misery, #sad, #sadness, and #sorrow for sadness #surprise and #surprised for surprise, and #disgust for disgust. We assume this hashtags to denote the label of the respective tweets to create a large dataset. We replace hashtags, URLs, and usernames by the same strings, respectively. The corpora trainRepr and testRepr are uniformly sampled randomly. We use these two corpora to train our emotion classifier and to evaluate the real world performance and modifier impact.