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--- |
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language: "en" |
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tags: |
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- roberta |
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- sentiment |
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- twitter |
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widget: |
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- text: "Oh no. This is bad.." |
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- text: "To be or not to be." |
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- text: "Oh Happy Day" |
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--- |
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This RoBERTa-based model can classify the sentiment of English language text in 3 classes: |
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- positive 😀 |
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- neutral 😐 |
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- negative 🙁 |
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The model was fine-tuned on 5,304 manually annotated social media posts. |
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The hold-out accuracy is 86.1%. |
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For details on the training approach see Web Appendix F in Hartmann et al. (2021). |
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# Application |
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```python |
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from transformers import pipeline |
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classifier = pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True) |
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classifier("This is so nice!") |
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``` |
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```python |
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Output: |
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[[{'label': 'negative', 'score': 0.00016451838018838316}, |
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{'label': 'neutral', 'score': 0.000174045650055632}, |
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{'label': 'positive', 'score': 0.9996614456176758}]] |
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``` |
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# Reference |
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Please cite [this paper](https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to reach out to [jochen.hartmann@tum.de](mailto:jochen.hartmann@tum.de) with any questions or feedback you may have. |
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``` |
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@article{hartmann2021, |
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title={The Power of Brand Selfies}, |
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author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded}, |
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journal={Journal of Marketing Research} |
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year={2021} |
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} |
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``` |
