Since The Eras Tour Film was just released, this time we're exploring Taylor Swift song data!

Are you Ready for It?

The taylor R package from W. Jake Thompson is a curated data set of Taylor Swift songs, including lyrics and audio characteristics. The data comes from Genius and the Spotify API.

There are three main datasets.

The first is taylor_album_songs, which includes lyrics and audio features from the Spotify API for all songs on Taylor’s official studio albums. Notably this excludes singles released separately from an album (e.g., Only the Young, Christmas Tree Farm, etc.), and non-Taylor-owned albums that have a Taylor-owned alternative (e.g., Fearless is excluded in favor of Fearless (Taylor’s Version)). We stan artists owning their own songs.

You can access Taylor’s entire discography with taylor_all_songs. This includes all of the songs in taylor_album_songs plus EPs, individual singles, and the original versions of albums that have been re-released as Taylor’s Version.

Finally, there is a small data set, taylor_albums, summarizing Taylor’s album release history.

Information on the audio features in the dataset from Spotify are included in their API documentation.

For your visualizations, the {taylor} package comes with it’s own class of color palettes, inspired by the work of Josiah Parry in the {cpcinema} package.

You might also be interested in the tayoRswift package by Alex Stephenson, a ggplot2 color palette based on Taylor Swift album covers. "For when your colors absolutely should not be excluded from the narrative."

Data Dictionary

taylor_album_songs.csv

According to a survey from March 2023 among U.S. adults, the most popular Taylor Swift album was "1989" with ***** percent of respondents stating that they liked the album. "Fearless" was also popular with another ***** percent saying that they enjoyed it. Less popular was Swift's album "Folklore" with which she surprised her fans during the Covid-19 pandemic.

TayLor Swift

## Overview

TayLor Swift is a dataset for object detection tasks - it contains Taylor annotations for 391 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Originally starting out in the country music genre, Taylor Swift has established herself as a pop mega star in recent years. In her ** studio albums she shows her musical range, covering genres from country to pop to Indie and folk music with a lot of commercial success. In the United Kingdom, her most popular song according to 'Swifties', as Swift's fans are called, is 'Shake it off' from her 2014 album.

English news that mention the "Taylor Swift". Crawled date: Oct, 2024. Documents count: 700.

According to an analysis from 2023, Taylor Swift has become a billionaire after making approximately *** million U.S. dollars through ticket sales and merchandise. Obviously, beside her super successful Era's tour, the other main component of her income is the new release and re-release of her music, with her re-recording previous albums in order to secure ownership of her work after the rights of her first music were sold.

Spotify data about Taylor Swift's songs. The streams were collected unitl April 24 2022

According to a survey conducted between September and October 2023, over ** percent of respondents stated that they were very or somewhat familiar with Taylor Swift. Only *** percent said that they were not at all familiar with the American singer.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset provides a comprehensive collection of setlists from Taylor Swift’s official era tours, curated expertly by Spotify. The playlist, available on Spotify under the title "Taylor Swift The Eras Tour Official Setlist," encompasses a diverse range of songs that have been performed live during the tour events of this global artist. Each dataset entry corresponds to a song featured in the playlist.

Taylor Swift, a pivotal figure in both country and pop music scenes, has had a transformative impact on the music industry. Her tours are celebrated not just for their musical variety but also for their theatrical elements, narrative style, and the deep emotional connection they foster with fans worldwide. This dataset aims to provide fans and researchers an insight into the evolution of Swift's musical and performance style through her tours, capturing the essence of what makes her tour unique.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on the setlist tracks by the artist. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow:

Authentication API Requests Data Cleaning and Transformation Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Taylor Swift)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Note: Popularity score reflects the score recorded on the day that retrieves this dataset. The popularity score could fluctuate daily.

Potential Applications:

• Predictive Analytics: Researchers might use this dataset to predict future setlist choices for tours based on album success, song popularity, and fan feedback.

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Taylor Swift's The Eras Tour setlist data, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

• Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset.
• Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws.
• No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.
• Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact on artists and the broader community.
• Data Accuracy and Timeliness: The dataset reflects a snapshot in time and may not represent the most current information available. Users are encouraged to verify the data's accuracy and timeliness.
• Source Verification: For the most accurate and up-to-date information, users are encouraged to refer directly to Spotify's official website.
• Independence Declaration: ...

What makes Taylor Swift so successful? And can developing artists harness the same techniques to jumpstart their next album development and release? A dataset for the analysis of relationships and elements common within Swift's catalogue of albums, from Spotify API data and Metacritic.

Includes album level data on:

Spotify Popularity Index

Spotify streaming numbers

Metacritic scores

Spotify algorithm metrics - acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, valance

Theme

Genre

Dataset

This dataset was created by Kailane Felix

Contents

Context

This data set was created by PromptCloud (a Data-as-a-Service provider), using the API exposed by Genius.com.

Content

It has the following data fields:

• album name
• track title
• track number
• lyric text
• line number of the lyric in the track
• year of release of the album

Initial analyses

You can check out this article to understand the following initial set of analysis:

– Exploratory analysis

• word counts based on tracks and albums
• time series analysis of word counts
• distribution of word counts

– Text mining

• word cloud
• bigram network
• sentiment analysis (includes chord diagram)

According to a study from February 2024, pop star Taylor Swift has won 32 American Music Awards in her career. In 2024, Swift made history by winning her fourth Grammy in the Album of the Year category. Overall, she has secured 14 wins of the prestigious award.

philipfourie/morse-taylor-swift dataset hosted on Hugging Face and contributed by the HF Datasets community

As of October 17, 2023, "Shake it Off" was Taylor Swift's most viewed video on Youtube, with over *** billion views. The music videos for "Blank Space" and "Bad Blood ft. Kendrick Lamar" ranked second and third, respectively.

This statistic presents information on the first week unit sales of Taylor Swift's albums from 2006 to 2017. Swift's fifth album '1989' sold 1.29 million copies during the first week after its release on October 27, 2014. Her most recent album, 'reputation', released in November 2017, sold 1.22 million copies in its first week in the United States

Taylor Swift is arguably one of the most popular and influential celebrities. Of particular interest, is her power as a role model for millions of fans across the world. Swift has spoken openly about different cultural issues, including her struggles with disordered eating and diet culture. Thus, the purpose of this paper was to explore whether Swift’s disclosures of her own eating and body image struggles impacted her fans. Over 200 TikTok and Reddit posts related to Swift and eating disorders or body image, as well as their associated comments were qualitatively coded to examine relevant themes. Key themes included: Swift as a role model for disordered eating recovery, use of or identification with specific Swift songs to illustrate struggles with eating or body image, continued objectification of Swift’s body, and conflicted reaction to a scene in Swift’s video for “Anti-Hero” that depicted the word “fat” on a scale. Overall, Swift’s disclosures of her own eating/body image struggles positively impacted her fans’ relationships with their eating behaviors and body image. This analysis illustrates the positive influence celebrities can have while also highlighting the limitations of personal disclosures to impact understanding of systemic issues like anti-fat bias.

A curated prompt template for AI language models: Here's how you can generate songs on suno in the style of your fav artist

inspired by: https://www.reddit.com/r/SunoAI/comments/1dg88p8/super_suno_prompts/

Taylor Swift Amerikalı country sanatçısı Taylor Swift in ilk stüdyo albümü 24 Ekim 2006 da Big Machine Records etiketiyl

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