Employee Sentiment Analysis

Overview

This project labels employee messages with sentiment, performs EDA, computes monthly sentiment scores, ranks employees, identifies flight risks (>=4 negative msgs in any 30-day window), and fits a simple linear regression for sentiment trend prediction.

Setup

1. Clone repo and put dataset in data/test(in).csv.
2. Install dependencies:

Employee Sentiment Analysis

Overview

This project labels employee messages with sentiment (Positive, Neutral, Negative), performs exploratory data analysis, computes monthly sentiment scores, ranks employees, identifies potential flight risks (≥4 negative messages in any 30-day window), and predicts sentiment trends with a linear regression model.

Project Structure

employee-sentiment-analysis/ ├── data/ │ └── test_in.csv # Input dataset ├── notebooks/ │ └── sentiment_analysis.ipynb # Main Jupyter Notebook ├── outputs/ │ ├── labeled_messages.csv │ ├── monthly_scores.csv │ ├── overall_ranking.csv │ ├── monthly_scores_sorted.csv │ ├── sentiment_trend_model.pkl ├── README.md └── requirements.txt

⚙️ Setup Instructions

1. Clone or download this repository.
2. Place the dataset in the data/ folder (rename if necessary).
3. Install dependencies:

   pip install -r requirements.txt
   python -m textblob.download_corpora