sentimentpredictor 0.1.0

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

Sentiment Analysis Classifier

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

Overview

sentimentclassifier 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 sentimentclassifier

Usage

Basic Usage

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

from sentiment_classifier import SentimentClassifier

# Initialize the classifier with the default model
classifier = SentimentClassifier()

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

Visualization

To visualize the sentiment distribution of a text:

from sentiment_classifier.visualization import plot_sentiment_distribution

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

CLI Usage

You can also use the package from the command line:

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

DataFrame Integration

Integrate with pandas DataFrames to classify text columns:

import pandas as pd
from sentiment_classifier.integration import DataFrameSentimentClassifier

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

classifier = DataFrameSentimentClassifier()
df = classifier.classify_dataframe(df, 'text')
print(df)

Sentiment Trends Over Time

Analyze and plot sentiment trends over time:

from sentiment_classifier.trends 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 sentiment_classifier.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 sentimentclassifier 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 sentiment_classifier.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.