Enhanced Sentiment Analysis Tool

A powerful and user-friendly command-line tool for analyzing the sentiment of text using state-of-the-art transformer models.

Features

🧠 Powerful NLP: Leverages Hugging Face transformer models for accurate sentiment analysis
🚀 High Performance: Automatic GPU acceleration when available
📊 Data Visualization: Generate charts and statistics from your analyses
🌈 Rich Output: Color-coded results with visual confidence indicators
📝 Comprehensive Logging: Track all analyses in a structured CSV format
📦 Batch Processing: Analyze multiple texts from files for efficiency
🛠️ Flexible Usage: Interactive mode or command-line options

Installation

Prerequisites

Python 3.7+
pip (Python package manager)

Setup

Clone this repository:

git clone <https://github.com/yourusername/sentiment-analysis-tool.git>
cd sentiment-analysis-tool

Install the required dependencies:

pip install -r requirements.txt

Or install them directly:

pip install transformers torch tqdm colorama matplotlib

Usage

Interactive Mode

Simply run the script without arguments for interactive mode:

python sentiment_analyzer.py

Enter text at the prompt to analyze sentiment. Special commands:

stats: Display statistics about your session
viz: Generate and display a visualization of sentiment distribution
quit: Exit the program

Command-Line Arguments

python sentiment_analyzer.py --file input.txt --output results.csv --model cardiffnlp/twitter-roberta-base-sentiment

Available options:

-model: Specify the Hugging Face model to use (default: "cardiffnlp/twitter-roberta-base-sentiment")
-file: Process multiple texts from a file (one per line)
-output: Save results to a specified CSV file
-no-log: Disable automatic logging of results
-cpu: Force CPU usage even if GPU is available

Examples

Analyzing a Single Text

python sentiment_analyzer.py
> I absolutely love this new feature, it works perfectly!

Batch Processing From File

python sentiment_analyzer.py --file customer_reviews.txt --output sentiment_results.csv

Using a Different Model

python sentiment_analyzer.py --model distilbert-base-uncased-finetuned-sst-2-english

Advanced Usage

Integration in Python Scripts

You can import and use the SentimentAnalyzer class in your own Python code:

from sentiment_analyzer import SentimentAnalyzer

analyzer = SentimentAnalyzer()
result = analyzer.analyze("I'm really enjoying this new software!")
print(f"Sentiment: {result['sentiment']}, Score: {result['score']}")

Processing Multiple Texts Efficiently

texts = ["Great product!", "Not satisfied with the quality", "It's okay, but not amazing"]
results = analyzer.analyze_batch(texts)

Logging and Data Analysis

By default, all analyses are logged to sentiment_log.csv with timestamps, which allows for:

Tracking sentiment trends over time
Building datasets for further analysis
Generating comprehensive reports

Visualization

The tool can generate pie charts showing the distribution of sentiments in your analyzed texts.

Dependencies

transformers: State-of-the-art NLP models
torch: Deep learning framework
tqdm: Progress bars for batch processing
colorama: Terminal text coloring
matplotlib: Data visualization

Contributing

Contributions are welcome!

Acknowledgments

Hugging Face for their excellent transformers library
Cardiff NLP for the pre-trained sentiment analysis model

License

Distributed under the GNU Affero General Public License v3.0 License. See LICENSE for more information.