Emotion classification identifies the emotional state of a writer or speaker. This is distinct from sentiment classification, which describes the viewpoint of writers toward their subjects.
Input:
讨厌！你骗我！
Output:
Angry
- Accuracy of classification.
- F1 score.
Cheng et al 2017 introduce an emotion corpus for Chinese Microblogs. It consists of short posts marked with the following distribution of emotion tags: Joy (11.3%), Angry (3.5%), Sad (2.6%), Fearful (0.6%), Positive (8.2%), Neutral (4.4%), Negative (9.9%), Non-emotion (59.5%). Furthermore, the corpus identifies the sub-span of the post that is the cause of the emotion.
Chen et al 2018 reports that the corpus includes “~3,000 subtweets, ~11,000 instances for EClass, and ~13,000 instances for ECause.”
| Test set | Genre |
|---|---|
| Cheng emotion corpus | Microblog |
- F1 score.
| System | F1 |
|---|---|
| [Chen et al 2018] | 62.4 |
| [Cheng et al 2017] | 58.2 |
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