thomasrenault/emotion

A multi-label emotion intensity classifier fine-tuned on US tweets, campaign speeches and congressional speeches. Built on distilbert-base-uncased with GPT-4o-mini annotation via the OpenAI Batch API.

Labels

The model predicts 8 independent emotion intensities (sigmoid, range 0–1):

| Label | |---|---| | anger | | sadness | | fear | | disgust | | pride | | joy | | gratitude | | hope |

Scores are independent — multiple emotions can be high simultaneously.

Training

Setting	Value
Base model	distilbert-base-uncased
Architecture	DistilBertForSequenceClassification (multi-label)
Problem type	multi_label_classification
Training data	~200,000 labeled documents
Annotation	GPT-4o-mini (temperature=0) via OpenAI Batch API
Epochs	4
Learning rate	2e-5
Batch size	16
Max length	512 tokens
Domain	US tweets about policy, campaign speeches and congressional floor speeches

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "thomasrenault/emotion"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

EMOTIONS = ["anger", "sadness", "fear", "disgust", "pride", "joy", "gratitude", "hope"]

def predict(text):
    enc = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    with torch.no_grad():
        probs = torch.sigmoid(model(**enc).logits).squeeze().tolist()
    return dict(zip(EMOTIONS, probs))

print(predict("We must stand together and fight for justice!"))

Intended Use

• Academic research on emotion in political communication
• Analysis of congressional speeches and social media
• Temporal trend analysis of emotional rhetoric

Limitations

• Trained exclusively on US English political text — performance may degrade on other domains
• Emotions are subjective; inter-annotator agreement on intensity scores is inherently noisy
• Labels are silver-standard (LLM-generated), not human-verified gold labels

Citation

If you use this model, please cite:

@misc{renault2025emotion,
  author    = {Renault, Thomas},
  title     = {thomasrenault/emotion: Multi-label emotion classifier for US political text},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/thomasrenault/emotion}
}