Edit model card

This RoBERTa-based model can classify expressed purchase intentions in English language text in 2 classes:

  • purchase intention 🤩
  • no purchase intention 😐

The model was fine-tuned on 2,000 manually annotated social media posts. The hold-out accuracy is 95% (vs. a balanced 50% random-chance baseline). For details on the training approach see Web Appendix F in Hartmann et al. (2021).

Application

from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/purchase-intention-english-roberta-large", return_all_scores=True)
classifier("I want this!")
Output:
[[{'label': 'no', 'score': 0.0014553926885128021},
  {'label': 'yes', 'score': 0.9985445737838745}]]

Reference

Please cite this paper when you use our model. Feel free to reach out to j.p.hartmann@rug.nl 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}
}
Downloads last month
256
Hosted inference API
Text Classification
Examples
Examples
This model can be loaded on the Inference API on-demand.