Predicting Customer Emotions From Product Reviews 💬

A Midterm Project for the Data Analytics Course — April 2025

---

Overview

Predicting Customer Emotions From Product Reviews is a midterm project developed in April 2025 for the Data Analytics course. The application aims to automatically classify customer emotions (Satisfied / Unsatisfied) based on their product reviews written in Vietnamese. This helps businesses improve service quality and better understand customer feedback.

---

Features

• Emotion Classification: Binary classification - Satisfied or Unsatisfied.
• Vietnamese NLP: Analyze customer feedback in Vietnamese using NLP techniques.
• Real-world Dataset: Collected from product reviews on Tiki.vn.
• Interactive Web App: Built with Streamlit for real-time prediction.

---

Technologies Used

• Python 3.11.12
• Logistic Regression for machine learning (88% accuracy).
• Pandas, Scikit-learn for data processing and modeling.
• Streamlit for building a user-friendly web interface.
• Under-the-hood NLP: Custom Vietnamese text preprocessing with normalization and word segmentation.

---

Live Demo

You can try the deployed application here:
🔗 https://predicting-constomer-emotion.streamlit.app/

---

⚙Installation

To run this project locally, follow these steps:

# 1. Clone the repository
git clone https://github.com/nglhongphuong/Predicting-Consumer-Emotions-from-Product-Reviews.git

# 2. Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# 3. Install dependencies
pip install -r requirements.txt

# 4. Run the app
streamlit run app.py

---

Project Structure

Your-Folder-Name/
├── app.py                      # Main entrypoint for Streamlit app
├── utils/
│   ├── __init__.py
│   └── func.py              
└── pages/
    ├── 1_Page_one.py          
    └── 2_Page_two.py

📚 Official Docs – Upgrade Python on Streamlit