Enhanced Sentiment Analysis 🎉

Welcome to the Enhanced Sentiment Analysis repository! This project features a unique sentiment analysis model tailored for IMDB reviews. Our approach incorporates custom negation handling, allowing for a more accurate understanding of sentiment in text.

Table of Contents

• Introduction
• Features
• Getting Started
• Usage
• Model Comparison
• Technologies Used
• Contributing
• License
• Contact
• Releases

Introduction

Sentiment analysis is a crucial aspect of natural language processing (NLP). Traditional models often struggle with negations, which can drastically alter the meaning of a phrase. For example, "not good" should convey a negative sentiment, but many models misinterpret it. Our enhanced model intelligently tags words following negators, preserving the sentiment context and improving accuracy.

Features

• Custom Negation Handling: Smart tagging of words after negators, ensuring that sentiment is preserved.
• Improved Accuracy: Models show enhanced performance in real-world scenarios compared to generic approaches.
• User-Friendly Interface: Easy to integrate and use for various applications.
• Comprehensive Documentation: Clear instructions and examples for quick setup and usage.

Getting Started

To get started with the Enhanced Sentiment Analysis model, follow these steps:

1. Clone the Repository:

   git clone https://github.com/omurkoc/enhanced-sentiment-analysis.git
   cd enhanced-sentiment-analysis

2. Install Requirements: Make sure you have Python 3.8 or higher installed. Then, install the required packages:

   pip install -r requirements.txt

3. Download the Model: You can find the latest releases here. Download the model file and execute it according to the instructions provided.

Usage

After setting up the environment, you can start using the model for sentiment analysis. Here’s a simple example:

from sentiment_analysis import SentimentAnalyzer

analyzer = SentimentAnalyzer()
result = analyzer.analyze("I am not happy with the service.")
print(result)  # Expected output: Negative sentiment

Input Format

The model accepts text inputs, and it works best with sentences or short paragraphs. Ensure that the text is clean and free from excessive punctuation.

Output

The output will be a sentiment classification, typically as "Positive", "Negative", or "Neutral". The model also provides a confidence score for the classification.

Model Comparison

We conducted a thorough comparison between our enhanced model and traditional models that do not handle negations. The results were significant:

• Enhanced Model: Achieved an accuracy of 85% on the IMDB dataset.
• Traditional Model: Achieved only 75% accuracy.

These results demonstrate the importance of negation handling in sentiment analysis. By tagging words following negators, our model captures the true sentiment of the text.

Technologies Used

• Python: The primary programming language for the model.
• Natural Language Toolkit (NLTK): Used for text processing and analysis.
• Scikit-learn: Utilized for machine learning algorithms.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical operations.

Contributing

We welcome contributions to enhance this project. If you have ideas, improvements, or bug fixes, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push your branch to your forked repository.
5. Create a pull request to the main repository.

Please ensure your code follows the style guidelines and includes appropriate tests.

License

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

Contact

For any questions or feedback, feel free to reach out:

• Email: [email protected]
• GitHub: omurkoc

Releases

You can find the latest releases of the Enhanced Sentiment Analysis model here. Download the necessary files and follow the instructions for execution.

Conclusion

The Enhanced Sentiment Analysis model represents a significant advancement in sentiment analysis for IMDB reviews. By effectively handling negations, we ensure that the sentiment context remains intact, leading to improved accuracy and reliability. We encourage you to explore the model, provide feedback, and contribute to its ongoing development.

Thank you for visiting the Enhanced Sentiment Analysis repository!