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Customer Emotion Analysis with Python

This project focuses on analyzing customer emotions using Natural Language Processing (NLP) and Machine Learning techniques in Python. The goal is to identify and analyze positive, negative, and neutral sentiments in customer feedback to derive valuable insights from textual data.

Features

• Text Processing: Preprocessing and cleaning textual data.
• Modeling: Utilizing machine learning models to analyze sentiments.
• Results Analysis: Visualization and interpretation of results.

Prerequisites

To run this project, you need to have the following Python packages installed:

• numpy
• pandas
• matplotlib
• scikit-learn
• nltk
• textblob

You can install these packages using pip:

pip install numpy pandas matplotlib scikit-learn nltk textblob

Usage

1. Clone the Repository:

   git clone https://github.com/MahradMozafari/Analyze-customer-emotions-with-Python.git

2. Run the Notebook:

   Navigate to the project directory and open the Emotion analysis.ipynb notebook using Jupyter Notebook.

   jupyter notebook Emotion\ analysis.ipynb

3. Analyze the Data:

   Follow the steps for preprocessing, model training, and sentiment analysis provided in the notebook.

Examples

The notebook includes examples of data preprocessing, model training, and results analysis.

Contributing

If you would like to contribute to this project, please report issues or suggest improvements by opening an issue on GitHub or submitting a pull request.

License

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

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