emotionclassifier 0.1.4

A flexible emotion classifier with support for multiple models

Text Emotion Classifier

A flexible emotion classifier package with support for multiple models, customizable preprocessing, visualization tools, fine-tuning capabilities, and more.

Overview

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.

Features

• 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.

Emotion Labels

• 😠 Anger
• 🤢 Disgust
• 😨 Fear
• 😊 Joy
• 😢 Sadness
• 😲 Surprise

Installation

You can install the package using pip:

pip install emotionclassifier

Usage

Basic Usage

Here'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'])

Batch Processing

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)

Visualization

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())

CLI Usage

You can also use the package from the command line:

emotionclassifier --model deberta-v3-small --text "I am very happy today!"

DataFrame Integration

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)

Emotion Trends Over Time

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-tuning

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')

Logging Configuration

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.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

This package uses pre-trained models from the Hugging Face Transformers library.

Contributing

Contributions are welcome! Please see the CONTRIBUTING file for guidelines on how to contribute to this project.

Links

• Documentation
• PyPI
• Source Code
• Issue Tracker