ananyajoshi-cseai/YouTube-Mood-Ring-Ananya
A Digital Vibe Checker for YouTube, developed by Ananya. This Python tool automates comment extraction via Google API and performs sentiment analysis using NLTK VADER. Generates a visual dashboard (Pie, Bar, Scatter, Time Series) to track community mood vs. engagement. Includes secure .env handling pagination for large datasets and CSV data export.
What's novel
A Digital Vibe Checker for YouTube, developed by Ananya. This Python tool automates comment extraction via Google API and performs sentiment analysis using NLTK VADER. Generates a visual dashboard (Pie, Bar, Scatter, Time Series) to track community mood vs. engagement. Includes secure .env handling pagination for large datasets and CSV data export.
Code Analysis
5 files read · 3 roundsFetches YouTube comments via API or CSV, classifies them with VADER sentiment analysis, and generates a 4-panel matplotlib dashboard (pie, bar, scatter, time series) plus an optional word cloud.
Strengths
The CSV loader in mood_ring_utils.py is genuinely flexible with column alias normalization and robust like-count parsing, and the code is clean, readable, and well-organized for its scope. The README accurately describes the implementation and the project is a complete, runnable end-to-end pipeline.
Weaknesses
The core logic is a thin wrapper around VADER and matplotlib with no novel algorithms or ML depth; the main script mixes fetching, analysis, and visualization in one file with a bare `except: pass` on CSV save, and tests only cover utility functions with no coverage of sentiment analysis or visualization logic.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
28
Contributors
2
Files
8
Active weeks
9
Repository
Language
Python
Stars
2
Forks
0
License
MIT