https://github.com/abdiasarsene/employee_emotion_analysis

https://github.com/abdiasarsene/employee_emotion_analysis

Last synced: 7 months ago
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• Host: GitHub
• URL: https://github.com/abdiasarsene/employee_emotion_analysis
• Owner: Abdiasarsene
• Created: 2025-03-03T16:09:36.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2025-03-03T16:18:50.000Z (8 months ago)
• Last Synced: 2025-03-03T17:28:06.896Z (8 months ago)
• Language: Python
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Employee Emotion Analysis

## 📌 Description  

This project aims to analyze employee well-being through internal surveys and anonymized exchanges (Slack, Teams, emails). The goal is to identify dominant emotions (stress, satisfaction, frustration, etc.) and provide recommendations to improve workplace quality of life.  

## 🛠️ Technologies Used  

- **Python**: Text analysis and NLP  

- **NLTK / SpaCy**: Text preprocessing  

- **VADER Sentiment/NRCLex**: Emotion classification  

- **Matplotlib / Seaborn**: Data visualization  

- **Pandas / NumPy**: Data manipulation  

## 📂 Project Structure  

```

📁 Employee-Emotion-Analysis  

│── 📂 data               # Raw and processed data  

│── 📂 notebooks          # Jupyter notebooks for exploratory analysis  

│── 📂 models             # Trained models for emotion classification  

│── 📂 reports            # Analysis reports and visualizations  

│── README.md             # Project documentation  

```

## 📊 Methodology  

1. **Data Collection**: Gathering and anonymizing survey responses and messages.  

2. **Text Preprocessing**: Cleaning, tokenization, lemmatization, stopword removal.  

3. **Emotion Analysis**: Using NLP models to classify emotions.  

4. **Visualization**: Trends over time, word clouds, heatmaps.  

5. **Recommendations**: Providing insights for workplace well-being improvement.  

## 📈 Sample Visualizations  

📌 *Example of emotion trends over six months:*  

![Sample Graph](./employee_emotion.png)  

## 📌 Future Work  

- 🔹 Refining NLP models for better accuracy.  

- 🔹 Extending the dataset for more robust analysis.  

- 🔹 Exploring correlations between emotions and company performance metrics.

## 📕 Download the full report

![Employee_Emotion_Analysis](Employee_Emotion_Analysis.pdf)

## 📜 License  

This project is licensed under the MIT License.