TSwift-Tunes

Exploring, Recommending & Searching Through Taylor Swift’s Music

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Welcome to the TSwift Tunes project! This project explores Taylor Swift’s music through data analysis and machine learning. It includes visualizations, a song recommender system, and a lyrics searcher, all powered by the tswift dataset. The project answers key questions about her songs and albums, analyzes audio features, and provides interactive tools for song recommendations and lyric searching.

Project Overview

Data Exploration and Visualization 👀

In this section, I explore Taylor Swift’s music through various visualizations, helping answer the following key questions:

These visualizations provide insights into Taylor Swift’s musical style, emotional tone, and the evolution of her discography.

Song Recommender 🎧

The Song Recommender system allows you to find similar songs from Taylor Swift’s catalog based on audio features. This component lets users select a song and receive recommendations for 5 similar songs that match the characteristics of the chosen track.

How to Use

Lyrics Searcher 🔍

The Lyrics Searcher allows you to search for specific lyrics in Taylor Swift’s songs using TF-IDF. This tool is inspired by Shayna Kothari’s Online Tool for Taylor Swift Lyrics, which provides a powerful way to analyze and query Taylor Swift’s lyrics.

Example Usage

Keyword Extraction 🔑

In this component, I perform keyword extraction for each song in Taylor Swift’s Lover album. I use the TF-IDF (Term Frequency-Inverse Document Frequency) method to identify the most significant words in the lyrics of each song.

How it Works:

How to Use

Contributions 🤝

This project was developed as part of my DSC 10 class at UC San Diego. The DSC 10 team provided invaluable support throughout the project, helping shape the direction of my analysis and guiding me with key insights. They provided essential data sources and offered hints to help me move forward with the project.

Furthermore, the DSC 10 team played a crucial role in shaping my analysis into a more accessible form by transforming it into interactive widgets and creating a user-friendly interface. Their expertise in data science and user experience was essential to making this project engaging and easy to interact with.

This project would not have been possible without the contributions and support from the DSC 10 team, and I am extremely grateful for their assistance throughout the process.

References and Data Sources 📖

Learning more about the data and analysis that inspired this project, check out Alice Zhao’s blog post A Data Scientist Breaks Down All 10 Taylor Swift Albums (The Extended Version). Below are links to all the resources I used in developing this project. Thanks to all the people who provided these resources!