TsegayIS122123/ethiopian-banking-app-sentiment-analysis

A comprehensive data analytics project analyzing user reviews from Google Play Store for Ethiopian banking apps. Using NLP and sentiment analysis to derive actionable insights for Commercial Bank of Ethiopia, Bank of Abyssinia, and Dashen Bank to improve customer satisfaction and app performance. # 🏦 Ethiopian Banking App Analytics A data-driven analysis of customer experience for Ethiopian banking mobile applications using Google Play Store reviews. This project leverages NLP and sentiment analysis to provide actionable insights for product improvement. ## 📊 Business Context **Omega Consultancy** is assisting three major Ethiopian banks in enhancing their mobile banking applications: - 🏦 **Commercial Bank of Ethiopia (CBE)** - 🏦 **Bank of Abyssinia (BOA)** - 🏦 **Dashen Bank** This analysis addresses critical business questions around user retention, feature enhancement, and complaint management. ## 🎯 Project Objectives - **Scrape & Analyze** 1,200+ user reviews from Google Play Store - **Quantify Sentiment** using DistilBERT model for precise emotion detection - **Identify Key Themes** in customer feedback through NLP techniques - **Provide Data-Driven Recommendations** for app improvement - **Build PostgreSQL Database** for structured data storage ## 🛠️ Technical Stack | Component | Technology | |-----------|------------| | **Data Collection** | `google-play-scraper` | | **Data Processing** | `pandas`, `numpy` | | **NLP & Analysis** | `transformers`, `torch`, `nltk` | | **Visualization** | `matplotlib`, `seaborn`, `wordcloud` | | **Database** | `PostgreSQL`, `psycopg2` | | **Environment** | `python-dotenv` | ## 🚀 Quick Start ### Prerequisites - Python 3.11+ - PostgreSQL ### Installation & Setup 1. **Clone the repository** ```bash git clone github.com cd ethiopian-banking-app-sentiment-analysis # Set up virtual environment - python -m venv .venv - .venv\Scripts\activate # install dependencies pip install -r requirements.txt ## Task 1: Data Collection & Preprocessing ### 📊 Results Summary - **Total Reviews Collected**: 1,350 raw → 1,244 clean reviews - **Reviews per Bank**: 414-415 reviews per bank (Target: 400+ ) - **Data Quality**: 0.00% missing data (Target: <5% ) - …