Document GitHub Sentiment Dataset Pipeline

[splimon]

Purpose

To create clear and reproducible documentation so future contributors can understand and rerun the GitHub sentiment pipeline without reverse‑engineering prior work. The notebooks should explain the commit/PR comment flow and produce outputs compatible with Kaiaulu.

Process

Create a documentation PR that:

• Adds the .yml files for the GitHub projects used in the dataset
• Adds the Python script used to load sentiment labels into MySQL
• Converts existing MySQL Workbench queries into Jupyter notebooks
• Organizes notebooks in a clear way:
  • Load sentiment CSV into MySQL
  • Explore DB + identify relevant tables
  • Scale/automate across all projects
• Export each notebook to a .py file so reviewers can read the code directly in GitHub (in addition to the .ipynb)
• Does not commit any data files (e.g., CSV exports, database dumps, generated data, etc.), only code and documentation (scripts, notebooks, configs)

The final notebook must output a file that can be inner joined with Kaiaulu output for the same project.

Task List

• Add .yml config project files to the repo
• Add Python loader script for loading sentiment labels into MySQL
• Create Notebook 1: load sentiment CSV into MySQL
• Create Notebook 2: explore DB + locate relevant tables/joins
• Create Notebook 3: scale/automate pipeline across all projects
• Export notebooks to .py for code review
• Ensure final notebook outputs a file that is joinable with Kaiaulu

[splimon]

4/3/26 Capstone Meeting Notes (Sentiment Classifier)

Hi @carlosparadis,

Next Steps

Notebook Documentation

• Use Python library calls to access and run SQL queries automatically in the notebooks (no manual SQL copy/paste)
• Display query results inline so users can validate outputs quickly
• Finalize the GitHub commit + PR comment documentation workflow. Use the notebooks as a guide so a new contributor can follow the notebook steps end-to-end without additional clarification
• After GitHub documentation is complete, apply the same workflow and documentation pattern to the Jira emotion dataset: Implement sentiment workflow with Jira Dataset emotion_jira_dataset#1