readme: ---
  language: "en"
  tags:
  - roberta
  - sentiment
  - twitter
  
  widget:
  - text: "Oh no. This is bad.."
  - text: "To be or not to be."
  - text: "Oh Happy Day"
  
  ---
  
  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}
  }
  ```