from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="gabe-zhang/twitter-emotion-bert",
)

classifier("I am so happy today!")
# [{'label': 'joy', 'score': 0.95}]

classifier("This makes me really angry")
# [{'label': 'anger', 'score': 0.89}]

Labels

ID	Emotion
0	sadness
1	joy
2	love
3	anger
4	fear
5	surprise

Performance

Overall: Accuracy 0.9385 | Weighted F1 0.9399

Emotion	Precision	Recall	F1-Score
Sadness	0.94	0.93	0.93
Joy	0.95	0.96	0.95
Love	0.86	0.89	0.87
Anger	0.93	0.90	0.91
Fear	0.91	0.89	0.90
Surprise	0.85	0.82	0.83

Training

Base model: bert-base-cased
Dataset: dair-ai/emotion (~20,000 English Twitter messages)
Optimizer: AdamW, learning rate 1e-5
Dropout: 0.5
Max sequence length: 512
Early stopping: patience 5
Class balancing: Random oversampling for minority classes
Framework: PyTorch Lightning

Limitations

Trained on English Twitter text only; may not generalize well to other languages or formal writing styles.
"Love" and "surprise" have lower F1 scores due to smaller representation in the training data and overlap with other emotions.
Short, informal text with slang or emojis may affect predictions.

Source Code

Full training code, notebooks, and baseline comparisons: github.com/gabe-zhang/twitter-emotion-classification