🌐 Customer Feedback Sentiment Analysis

The script essentially demonstrates how to perform sentiment analysis on data using, as well as how to calculate similarity between two pieces of text. However, please note that this script assumes the existence of a CSV file named 'amazon_product_reviews.csv' in the same directory as the script itself, containing a column named 'reviews.text' which holds the reviews to be analyzed.

📑Table of Contents

1. Features
2. Installation
3. Usage
4. Dependencies
5. Contribution

📈Features

• This Python script analyzes the sentiment of customer feedback using Natural Language Processing (NLP) techniques.
• It provides insights into the emotional tone of the feedback, helping businesses understand customer sentiment more effectively
• It classifies feedback as positive, negative, or neutral based on sentiment analysis.

Installation

• Clone this repository to your local machine:

  git clone https://github.com/yourusername/sentiment_analysis.git
  

• Navigate to the project directory:

  cd customer-feedback-sentiment-analysis
  

Usage

• Prepare your customer feedback data.
• The data should be in a format that the script can process (e.g., CSV, text file).
• Ensure the data in in the same folder as the python script
• Run the Python script!
• Analyze the generated output, which will contain sentiment analysis results.

Dependencies

You need to have the following libraries installed in your Python environment to run this code.

• spaCy: Python library for natural language processing
• SpacyTextBlob: SpacyTextBlob primarily focuses on sentiment analysis aspects of TextBlob

👯Contribution

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

• Fork the repository.
• Create a new branch
• Make your changes.
• Commit your changes
• Push to the branch
• Create a new Pull Request.