sentimentpredictor 0.1.3

A flexible sentiment analysis predictor package supporting multiple pre-trained models, customizable preprocessing, visualization tools, fine-tuning capabilities, and seamless integration with pandas DataFrames.

Sentiment Analysis Predictor

A flexible sentiment analysis predictor package supporting multiple pre-trained models, customizable preprocessing, visualization tools, fine-tuning capabilities, and seamless integration with pandas DataFrames.

Overview

sentimentpredictor is a Python package designed to classify sentiments in text using various pre-trained models from Hugging Face's Transformers library. This package provides a user-friendly interface for sentiment 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 sentiment distributions and trends over time.
• Fine-tuning Capability: Fine-tune models on your own datasets.
• User-friendly CLI: Command-line interface for quick sentiment classification.
• Integration with Data Platforms: Seamless integration with pandas DataFrames.
• Extended Post-processing: Additional utilities for detailed sentiment analysis.

Installation

You can install the package using pip:

pip install sentimentpredictor

Usage

Basic Usage

Here's an example of how to use the SentimentPredictor to classify a single text:

from sentimentpredictor import SentimentPredictor

# Initialize the predictor with the default model
predictor = SentimentPredictor()

# Classify a single text
text = "I am very happy today!"
result = predictor.predict(text)
print("Sentiment:", 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 = predictor.predict_batch(texts)
print("Batch processing results:", results)

Visualization

To visualize the sentiment distribution of a text:

from sentimentpredictor import plot_sentiment_distribution

result = predictor.predict("I am very happy today!")
plot_sentiment_distribution(result['probabilities'], predictor.labels.values())

CLI Usage

You can also use the package from the command line:

sentimentpredictor --model roberta --text "I am very happy today!"

DataFrame Integration

Integrate with pandas DataFrames to classify text columns:

import pandas as pd
from sentimentpredictor import DataFrameSentimentPredictor

df = pd.DataFrame({
    'text': ["I am very happy today!", "I am so sad."]
})

predictor = DataFrameSentimentPredictor()
df = predictor.classify_dataframe(df, 'text')
print(df)

Sentiment Trends Over Time

Analyze and plot sentiment trends over time:

from sentimentpredictor import SentimentAnalysisTrends

texts = ["I am very happy today!", "I am feeling okay.", "I am very sad."]
trends = SentimentAnalysisTrends()
sentiments = trends.analyze_trends(texts)
trends.plot_trends(sentiments)

Fine-tuning

Fine-tune a pre-trained model on your own dataset:

from sentimentpredictor.fine_tune import fine_tune_model

# Define your train and validation datasets
train_dataset = ...
val_dataset = ...

# Fine-tune the model
fine_tune_model(predictor.model, predictor.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.

Running Tests

Run the tests using pytest:

poetry run pytest

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