language:
- en
tags:
- roberta
- marketing mix
- multi-label
- classification
- microblog
- tweets
widget:
- text: Tesla's Cybertruck is way overpriced!
- text: Why are Apple's new Airpods not available in BestBuy's online store?
- 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:
- Product
- Place
- Price
- 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
Download the paper "Creating Synthetic Experts with Generative AI"