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---
language:
- en
tags:
- roberta
- marketing mix
- multi-label
- classification
- microblog
- tweets
widget:
- text: "Nike needs to sponsor more e-sports atheletes with gear!"
- text: "Why are they always sold-out of Apple's new AirPods on their ponline shop?"
- text: "It's going to rain later."
---
# Model Card for: mmx_classifier_microblog_ENv02
Multi-label classifier that identifies which marketing mix variable(s) a microblog post pertains to.
## Model Details
You can use this classifier to determine which of the 4P's of marketing, also known as marketing mix variables, a microblog post (e.g., Tweet) pertains to:
1. Product
2. Place
3. Price
4. Promotion
### Model Description
This classifier is a fine-tuned checkpoint of [cardiffnlp/twitter-roberta-large-2022-154m] (https://huggingface.co./cardiffnlp/twitter-roberta-large-2022-154m).
It was trained on 15K Tweets that mentioned at least one of 699 brands. The Tweets were cleaned and labeled using OpenAI's GPT4.
Because this is a multi-label classification problem, we use binary cross-entropy (BCE) with logits loss for the fine-tuning. We basically combine a sigmoid layer with BCELoss in a single class.
To obtain the probabilities for each label (i.e., marketing mix variable), you need to "push" the predictions through a sigmoid function. This is already done in the accompanying python notebook.
IMPORTANT: At the time of writing this description, Huggingface's pipeline did not support multi-label classifiers.
### Citation
For attribution, please cite the following reference if you use this model:
```
Ringel, Daniel, Creating Synthetic Experts with Generative Artificial Intelligence (July 15, 2023). Available at SSRN: https://ssrn.com/abstract=4542949
```
### Working Paper
Download the working paper from SSRN: ["Creating Synthetic Experts with Generative AI"](https://papers.ssrn.com/abstract_id=4542949)
### Additional Ressources
[www.synthetic-experts.ai](http://www.synthetic-experts.ai)
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