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
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix) 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.