metadata

language: en
tags:
  - roberta
  - sentiment
  - twitter
widget:
  - text: This looks tasty. Where can I buy it??
  - text: Now I want this, too.
  - text: You look great today!
  - text: I just love spring and sunshine!

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 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}
}