lyrics-analysis

LyricMind is an AI-powered cognitive firewall that analyzes song lyrics to protect against unconscious absorption of harmful content. Using customizable frameworks and LLMs, it detects risky patterns and integrates with music players to trigger protective actions when thresholds are exceeded.

See how a rapper rides the beat: a free app for macOS and Windows that maps every syllable to the beat grid, with rhyme groups, pocket timing and flow metrics. Runs 100% locally.

Music AI web-based application.

This project merges Natural Language Processing with music generation, using NLP techniques to analyze lyrics and guide the creation of music. It employs sentiment analysis, keyword extraction, and topic modeling, along with Genius and Spotify APIs, to generate music that reflects the emotional depth of the lyrics using the MusicGen model.

Data exploration of the personality changes over time in the song writing of Coldplay, using Watson Personality Insights. Original sketches by @mcconville

Corpus-linguistics comparison of Cher vs Robyn lyrics + Twitter bios in Python: NLTK tokenization, lexical diversity (0.103 vs 0.142 over 35,916 vs 15,227 tokens), concentration ratios, emoji handling, word clouds; Jupyter notebooks, pandas, pytest + GitHub Actions CI.

My DS experiments with lyrics.

🎶 Python / Jupyter toolkit for parsing and analyzing song lyrics — built for NLP and data exploration

Large-scale music discovery project that clusters Spotify tracks using audio features and lyrics, compares MiniBatchKMeans, DBSCAN, and hierarchical clustering, and demonstrates content-based recommendation.

Taylor Swift music analysis with visualizations, lyric search, and a content-based song recommender.

An NLP-based system for song lyrics analysis, genre classification, and topic modeling using TF-IDF, Linear SVM, and LDA.

End-to-end Spotify analytics project combining lyrics NLP, sentiment analysis (VADER), profanity detection, emotion analysis, and AWS Rekognition album cover analytics.

An interactive Streamlit dashboard to analyze Billboard Top 100 songs (2012–2022) by genre, artist, lyrics, and sentiment using data visualization and NLP. Includes filtering, trend analysis, word clouds, sentiment insights, and CSV export.

Interactive NLP dashboard exploring lyric sentiment, model disagreement, repetition sensitivity, and yearly patterns.

The Chinese lyrics HSK analyzer

Data science project analyzing Turkish song lyrics using NLP, sentiment analysis and clustering.

Computational analysis of Odd Future lyrics across eras using NLP, emotion, collocation, topic, place-name, and vocabulary trends.

Do song lyrics in different regions emphasise different emotional or thematic patterns? What are the top regions for different emotions? Are the top mandarin songs all about heartache / longing? A full project from ingestion > translation > NLP classification > analysis on databricks