Project Description

The Employee Feedback Analyzer is a Python-based tool designed to analyze employee feedback data, perform sentiment analysis, and generate comprehensive reports with visualizations. This tool helps organizations gain insights from employee feedback by identifying sentiment patterns, extracting key topics, and visualizing department-wise performance metrics.

The system processes feedback data, categorizes sentiments as positive, negative, or neutral using TextBlob's natural language processing capabilities, and generates various charts and reports to help management understand employee satisfaction across different departments.

Features

• Feedback Data Generation : Creates dummy feedback data for testing and demonstration
• Sentiment Analysis : Analyzes feedback text to determine positive, negative, or neutral sentiment
• Keyword Extraction : Identifies most common words in feedback by sentiment category
• Department-wise Analysis : Breaks down feedback by department for targeted insights
• Comprehensive Visualizations : Generates multiple charts and graphs including:
• Sentiment distribution (pie and bar charts)
• Rating distribution
• Department-wise sentiment analysis
• Department performance metrics
• Keyword frequency analysis
• Department-specific keyword analysis
• Interactive Department Selection : Filter analysis by specific departments
• Report Generation : Creates JSON, CSV, and visual reports

Installation

1. Clone the repository:

   git clone cd employee-feedback-analyzer

2. Create and activate a virtual environment (recommended):

   python -m venv venv

   On Windows

   venv\Scripts\activate

   On macOS/Linux

   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

How to Run

1. Run the main application:

   python main.py

2. The program will:

   • Generate dummy feedback data in data/feedback.csv
   • Perform sentiment analysis and save results to data/feedback_with_sentiment.csv
   • Generate reports and visualizations in the reports folder
   • Prompt you to select a specific department for analysis (optional)

3. Command-line options:

   Run with versioned reports (includes timestamp)

   python main.py --versioned

   Run with a specific department filter

   python main.py --department IT

4. View the generated reports in the reports folder

Sample Screenshots

Sentiment Distribution

Department Performance Insights

Keyword Analysis

Department-specific Keywords

Project Structure

• main.py : Main application entry point
• src/
• analyzer.py : Core analysis functions including keyword extraction
• data_generation.py : Generates dummy feedback data
• report_generator.py : Creates visualizations and reports
• sentiment_analyzer.py : Performs sentiment analysis using TextBlob
• data/ : Contains generated feedback data
• reports/ : Contains generated reports and visualizations

Dependencies

See requirements.txt for a complete list of dependencies. Key libraries include:

• pandas: Data manipulation and analysis
• matplotlib: Data visualization
• textblob: Natural language processing and sentiment analysis
• nltk: Natural language toolkit for keyword extraction

Future Enhancements

• Add real-time feedback collection interface
• Implement more advanced NLP techniques for better sentiment analysis
• Create interactive dashboard for exploring feedback data
• Add trend analysis to track changes over time
• Implement user authentication for accessing reports