
This RoBERTa-based model can classify the sentiment of English language text in 3 classes:

- positive 😀
- neutral 😐
- negative 🙁

The model was fine-tuned on 5,304 manually annotated social media posts. 
The hold-out accuracy is 86.1%. 
For details on the training approach see Web Appendix F in Hartmann et al. (2021). 

# Application
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/sentiment-roberta-large-english-3-classes", return_all_scores=True)
classifier("This is so nice!")
```

```python
Output:
[[{'label': 'negative', 'score': 0.00016451838018838316},
  {'label': 'neutral', 'score': 0.000174045650055632},
  {'label': 'positive', 'score': 0.9996614456176758}]]
```

# Reference
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.
```
@article{hartmann2021,
  title={The Power of Brand Selfies},
  author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},
  journal={Journal of Marketing Research}
  year={2021}
}
```
{"language": "en", "tags": ["roberta", "sentiment", "twitter"], "widget": [{"text": "Oh no. This is bad.."}, {"text": "To be or not to be."}, {"text": "Oh Happy Day"}]}
transformers pytorch roberta text-classification sentiment twitter en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roberta #text-classification #sentiment #twitter #en #autotrain_compatible #endpoints_compatible #has_space #region-us 


This RoBERTa-based model can classify the sentiment of English language text in 3 classes:

- positive 
- neutral 
- negative 

The model was fine-tuned on 5,304 manually annotated social media posts. 
The hold-out accuracy is 86.1%. 
For details on the training approach see Web Appendix F in Hartmann et al. (2021). 

# Application




# Reference
Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@URL with any questions or feedback you may have.

# Application # Reference
Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@URL with any questions or feedback you may have.
TAGS
#transformers #pytorch #roberta #text-classification #sentiment #twitter #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Application # Reference
Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@URL with any questions or feedback you may have.
passage: TAGS
#transformers #pytorch #roberta #text-classification #sentiment #twitter #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Application# Reference
Please cite this paper when you use our model. Feel free to reach out to jochen.hartmann@URL with any questions or feedback you may have.