A flexible emotion classifier package with support for multiple models, customizable preprocessing, visualization tools, fine-tuning capabilities, and more.
emotionclassifier is a Python package designed to classify emotions in text using various pre-trained models from Hugging Face's Transformers library. This package provides a user-friendly interface for emotion classification, along with tools for data preprocessing, visualization, fine-tuning, and integration with popular data platforms.
- Multiple Model Support: Easily switch between different pre-trained models.
- Customizable Preprocessing: Clean and preprocess text data with customizable functions.
- Visualization Tools: Visualize emotion distributions and trends over time.
- Fine-tuning Capability: Fine-tune models on your own datasets.
- User-friendly CLI: Command-line interface for quick emotion classification.
- Integration with Data Platforms: Seamless integration with pandas DataFrames.
- Extended Post-processing: Additional utilities for detailed emotion analysis.
- 😠 Anger
- 🤢 Disgust
- 😨 Fear
- 😊 Joy
- 😢 Sadness
- 😲 Surprise
You can install the package using pip:
pip install emotionclassifierHere's an example of how to use the EmotionClassifier to classify a single text:
from emotionclassifier import EmotionClassifier
# Initialize the classifier with the default model
classifier = EmotionClassifier()
# Classify a single text
text = "I am very happy today!"
result = classifier.predict(text)
print("Emotion:", result['label'])
print("Confidence:", result['confidence'])You can classify multiple texts at once using the predict_batch method:
texts = ["I am very happy today!", "I am so sad."]
results = classifier.predict_batch(texts)
print("Batch processing results:", results)To visualize the emotion distribution of a text:
from emotionclassifier import plot_emotion_distribution
result = classifier.predict("I am very happy today!")
plot_emotion_distribution(result['probabilities'], classifier.labels.values())You can also use the package from the command line:
emotionclassifier --model deberta-v3-small --text "I am very happy today!"Integrate with pandas DataFrames to classify text columns:
import pandas as pd
from emotionclassifier import DataFrameEmotionClassifier
df = pd.DataFrame({
'text': ["I am very happy today!", "I am so sad."]
})
classifier = DataFrameEmotionClassifier()
df = classifier.classify_dataframe(df, 'text')
print(df)Analyze and plot emotion trends over time:
from emotionclassifier import EmotionTrends
texts = ["I am very happy today!", "I am feeling okay.", "I am very sad."]
trends = EmotionTrends()
emotions = trends.analyze_trends(texts)
trends.plot_trends(emotions)Fine-tune a pre-trained model on your own dataset:
from emotionclassifier.fine_tune import fine_tune_model
# Define your train and validation datasets
train_dataset = ...
val_dataset = ...
# Fine-tune the model
fine_tune_model(classifier.model, classifier.tokenizer, train_dataset, val_dataset, output_dir='fine_tuned_model')By default, the sentimentpredictor package logs messages at the WARNING level and above. If you need more detailed logging (e.g., for debugging), you can set the logging level to INFO or DEBUG:
from sentimentpredictor.logger import set_logging_level
# Set logging level to INFO
set_logging_level('INFO')
# Set logging level to DEBUG
set_logging_level('DEBUG')You can set the logging level to one of the following: DEBUG, INFO, WARNING, ERROR, CRITICAL.
This project is licensed under the MIT License - see the LICENSE file for details.
This package uses pre-trained models from the Hugging Face Transformers library.
Contributions are welcome! Please see the CONTRIBUTING file for guidelines on how to contribute to this project.