language: en
tags:
- distilroberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: Oh wow. I didn't know that.
- text: This movie always makes me cry..
- text: Oh Happy Day
Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class:
- anger 🤬
- disgust 🤢
- fear 😨
- joy 😀
- neutral 😐
- sadness 😭
- surprise 😲
The model is a fine-tuned checkpoint of DistilRoBERTa-base.
Application 🚀
a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab:
b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab:
Contact 💻
Please reach out to jochen.hartmann@uni-hamburg.de if you have any questions or feedback.
Thanks to Samuel Domdey and chrsiebert for their support in making this model available.
Appendix 📚
Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets.
| Name | anger | disgust | fear | joy | neutral | sadness | surprise |
|---|---|---|---|---|---|---|---|
| Crowdflower (2016) | Yes | - | - | Yes | Yes | Yes | Yes |
| Emotion Dataset, Elvis et al. (2018) | Yes | - | Yes | Yes | - | Yes | Yes |
| GoEmotions, Demszky et al. (2020) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| ISEAR, Vikash (2018) | Yes | Yes | Yes | Yes | - | Yes | - |
| MELD, Poria et al. (2019) | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| SemEval-2018, EI-reg (Mohammad et al. 2018) | Yes | - | Yes | Yes | - | Yes | - |
The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.
The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). The evaluation accuracy on a holdout test set is 66% (and significantly above the random-chance baseline of 1/7 = 14%).