Analyrics

This talk explores building a full-stack lyrics analysis platform using NLP techniques for sentiment, emotion, and collaboration insights, supported by scalable web scraping and interactive visualizations.

Overview

25,000+ Users | Analyrics: Lyrics Analysis Platform analyrics.info, github.com/shah-aryan/Analyrics
Built and deployed “Analyrics,” a full-stack web application with a lyrics analysis database of 1M+ songs, artists, and albums, using MongoDB, Express.js, React, Tailwind, RESTful APIs, and Node.js for low latency and well-received iconic UI
Developed efficient web scrapers for 1M+ web pages, implementing concurrency in Python, leveraging rate-limiting to avoid IP bans, and employing robust error handling, data validation, and retry mechanisms to ensure data integrity and reliability
Implemented NLP algorithms for lyrics analysis, including sentiment and emotion analysis with NRCLex and part of speech recognition with spaCy to identify emotions, vocabularies, trends, sentiments, and collaborations in music
Visualized interactive artist collaboration maps, emotion analyses, reading levels, and word clouds creatively using D3.js
Received positive feedback from users and tech companies (Apple, Spotify, Genius, Dots(YC21)), contributing to 25,000+ users

Links

Tech stack