SentimentScope for Amazon Reviews 🌟

Introduction 📘

Welcome to "SentimentScope for Amazon Reviews"! This project harnesses the power of Natural Language Processing (NLP) to analyze sentiments in Amazon product reviews. It uses Python libraries like spaCy and TextBlob to understand customer emotions, categorizing them as positive, negative, or neutral.

Features 🛠

• Sentiment Analysis: Analyzes sentiments expressed in Amazon product reviews.
• NLP with spaCy: Employs spaCy for efficient language processing.
• Sentiment Polarity with TextBlob: Integrates TextBlob to compute sentiment polarity.
• Large Dataset Processing: Capable of handling extensive datasets effectively.

Installation 💻

To get started with this project, clone the repository and install the required packages:

git clone https://github.com/AdrianGeorgeM/SentimentScope-for-Amazon-Reviews.git
cd SentimentScope-for-Amazon-Reviews
pip install -r requirements.txt

How to Use 🔍

Run the sentiment analysis with the following command:

python sentiment_analysis.py

Data 📈

The script processes amazon_product_reviews.csv, which contains an array of product reviews, focusing particularly on the reviews.text field for sentiment analysis. Note: The dataset is not included in this repository due to privacy and size considerations.

Output 📊

The output is a CSV file named sentiment_analysis_output.csv, containing the original reviews and their respective sentiment scores.

Contributing 🤝

Contributions are what make the open-source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated. Feel free to fork the project, submit pull requests, or send suggestions to enhance the functionality or efficiency.

License 📜

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments 💐

• Heartfelt thanks to the creators of spaCy and TextBlob for their outstanding work in NLP.
• Inspired by the collaborative spirit of the Python open-source community.