PSLF Discussion Analysis

Overview

This project analyzes Public Service Loan Forgiveness (PSLF) discussions across multiple platforms, with a focus on medical professionals and healthcare workers. The analysis includes sentiment analysis, time series trends, policy impact assessment, and comprehensive data visualizations.

Key Features

• Multi-Platform Data Collection: Reddit (r/PSLF, r/medicalschool, r/medicine, r/StudentLoans, r/personalfinance) and Student Doctor Network
• Sentiment Analysis: TextBlob-based polarity and subjectivity analysis
• Time Series Analysis: Temporal trends, seasonal patterns, and policy impact assessment
• Medical Professional Focus: Specialized analysis for healthcare workers and medical trainees
• Comprehensive Visualizations: 12-panel analytics dashboard with word clouds and trend analysis
• Statistical Modeling: Trend analysis and basic forecasting

Analysis Results

Dataset Overview

• 322 total posts analyzed across platforms
• 40.7% medical-related discussions
• Date range: 2012-2025 (13+ years of data)
• Overall positive sentiment (0.088 average polarity)

Key Insights

• Medical professionals show 5x higher engagement than general users
• Policy changes significantly impact discussion volume and sentiment
• Academic calendar correlation: Medical discussions peak during training transitions
• Seasonal patterns: August peak (residency/fellowship season), October low

Technology Stack

• Python 3.9+
• Data Analysis: pandas, numpy, scipy, scikit-learn
• Visualization: matplotlib, seaborn, wordcloud
• Text Processing: NLTK, TextBlob
• Web Scraping: requests, BeautifulSoup
• API Integration: Reddit API (PRAW)

Project Structure

PSLF/
├── notebook.ipynb                          # Main analysis notebook
├── pyproject.toml                          # Poetry configuration
├── combined_pslf_discussions.csv           # Multi-platform dataset
├── enhanced_pslf_discussions.csv           # Enhanced search dataset
├── reddit_pslf_discussions.csv             # Reddit-specific data
├── sdn_pslf_discussions.csv               # Student Doctor Network data
└── README.md                              # This file

Getting Started

Prerequisites

• Python 3.9 or higher
• Poetry (for dependency management)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/PSLF-analysis.git
cd PSLF-analysis

2. Install dependencies using Poetry:

poetry install

3. Activate the virtual environment:

poetry shell

4. Launch Jupyter Notebook:

jupyter notebook

5. Open notebook.ipynb and run the cells

Analysis Components

1. Data Collection

• Reddit API integration for real-time data
• Student Doctor Network scraping
• Enhanced search terms for comprehensive coverage

2. Sentiment Analysis

• TextBlob polarity scoring (-1 to +1)
• Categorical classification (positive, negative, neutral)
• Topic-specific sentiment analysis

3. Time Series Analysis

• Monthly discussion volume trends
• Sentiment evolution over time
• Policy impact assessment
• Seasonal pattern identification

4. Medical Professional Analysis

• Healthcare-specific keyword filtering
• Medical vs general user comparison
• Academic calendar correlation analysis

5. Visualization Dashboard

• 12-panel comprehensive analytics
• Word clouds for different categories
• Time series plots and trend analysis
• Engagement metrics and platform comparisons

Policy Impact Analysis

The analysis includes assessment of major PSLF policy changes:

• 2017-10-01: PSLF Program First Eligible Date
• 2018-09-01: TEPSLF Program Launch
• 2020-03-01: COVID-19 Payment Pause
• 2021-10-01: Limited PSLF Waiver Announced
• 2022-10-31: Limited PSLF Waiver Deadline
• 2023-07-01: New SAVE Income Plan Launch

Key Findings

1. Volume Growth: Strong upward trajectory, especially since 2022
2. Policy Sensitivity: Clear response to PSLF policy changes
3. Academic Seasonality: Medical discussions peak during training transitions
4. Sentiment Stability: Generally positive with improving trend
5. Platform Dynamics: Medical subreddits show different engagement patterns

Future Enhancements

• Real-time data pipeline integration
• Interactive dashboard development
• Advanced NLP topic modeling
• Predictive analytics for policy impact
• Cross-platform sentiment comparison

📄 License

This project is open source and available under the MIT License.

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This analysis was conducted for research purposes to understand public discourse around PSLF policy and its impact on medical professionals and public service workers.