everyl12/crisis_emotion_roberta

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

crisisemotionroberta

This emotion classification model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on a dataset of 9,300 tweets in the Flint Water Crisis (Wu, Wong, Zhao, & Liu, 2021). It achieves the following results on the testing set: 0.75 accuracy, 0.74 weighted accuracy, and 0.68 macro accuracy.

Classify the primary emotion of a crisis tweet into one of the following 6 categories (The F-1 score for each emotion):

1. 1.Anger (0.83)
2. 2.Sadness (0.67)
3. 3.Joy (0.69)
4. 4.Sympathy (0.80)
5. 5.Sarcasm (0.44)
6. 6.Neutral (0.64)

To cite our work: Wu, J., Wong, C.-W., Zhao, X., & Liu, X. (2021). Toward effective automated content analysis via crowdsourcing. Paper presented at the IEEE International Conference on Multimedia and Expo (ICME). https://doi.org/10.1109/ICME51207.2021.9428220

Intended uses & limitations

For classifying the emotion of English tweets during crises & disasters

Training and evaluation data

Dataset: 9,300 tweets in the Flint water crisis. Each tweet was labeled by trained & qualified crowdsourcing workers for 3-5 times. For detail, see our IEEE ICME paper - Wu, Wong, Zhao, & Liu, 2021. (https://arxiv.org/pdf/2101.04615.pdf)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 15

Training results

Framework versions

• —Transformers 4.23.0.dev0
• —Pytorch 1.13.0.dev20220917+cu117
• —Datasets 2.4.0
• —Tokenizers 0.12.1