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

💁‍♀️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: ...

Dataset has a csv file based on the Wikipedia page of Taylor Swift. Table has two columns context of length 800 and next sentence to train LLM

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

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

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

Dataset

This dataset was created by Kailane Felix

Contents

As of April 5, 2024, the concert movie “Taylor Swift: The Eras Tour” garnered around 262 million U.S. dollars worldwide and about 181 million dollars domestically during its release weekend. "The Eras Tour" is holding first place as the highest-grossing concert movie ever made, followed by "Justin Bieber: Never Say Never", released in 2011.

O presente trabalho objetiva investigar como se dá a construção de sentido através da interação (leitura e escuta) com canções (letra e melodia). Para tanto, utilizamos a metodologia do Mapeamento da Experiência Estética (MAPEE), metodologia definida por Santos e Costa (2020) com base na articulação elaborada por Santos (2007; 2009) entre a Teoria do Efeito Estético, de Wolfgang Iser e a Teoria Histórico-Cultural, de L. S. Vygotsky. Selecionamos como corpus três canções de Taylor Swift, quais sejam Cardigan, August e Betty (2020). A comparação entre os mapeamentos trouxe à luz evidências de que o processo de identificação dos conceitos de Iser na experiência estética do autor deste trabalho toma dois caminhos entrecruzados: ao passo que interpreta-se a letra, há constante intercorrência e percepção da interpretação (voz) da cantora e da mudança dos instrumentais, o que configura camadas diferentes nos mapeamentos, podendo tanto serem analisadas de forma interligadas e conjuntas, como foi feito neste trabalho, como também de forma individual, caso fosse considerada apenas a letra (poesia) para atribuição de sentido. Também foi possível prospectarmos que usar a música em sala de aula pode facilitar um trabalho interdisciplinar, visto seu caráter sonoro e lírico, com o ensino de leitura literária em vários níveis: compreensão de texto, sensibilidade musical, desenvolvimento do gosto pelo canto e pelo instrumental e, no caso em tela como as canções foram de âmbito internacional: intercâmbio com outros idiomas e culturas. Portanto, a análise destas canções através da metodologia do Mapeamento da Experiência Estética estabeleceu uma nova camada de consciência na leitura de uma letra (poesia) e escuta cantada. Assim, atentar para a interação do/a discente com a música e verificar que os eventos descritos na teoria iseriana articulada à vygotskiana também ocorrem na mente do/a ouvinte parece importante via de acesso aos procedimentos metacognitivos de construção de sentido na audiência de canções, podendo funcionar como trajeto de acesso a interação com outros suportes ficcionais.

The statistic provides data on favorite music genres among consumers in the United States as of July 2018, sorted by age group. According to the source, 52 percent of respondents aged 16 to 19 years old stated that pop music was their favorite music genre, compared to 19 percent of respondents aged 65 or above. Country music in the United States – additional information

In 2012, country music topped the list; 27.6 percent of respondents picked it among their three favorite genres. A year earlier, the result was one percent lower, which allowed classic rock to take the lead. The figures show, however, the genre’s popularity across the United States is unshakeable and it has also been spreading abroad. This could be demonstrated by the international success of (among others) Shania Twain or the second place the Dutch country duo “The Common Linnets” received in the Eurovision Song Contest in 2014, singing “Calm after the storm.”

The genre is also widely popular among American teenagers, earning the second place and 15.3 percent of votes in a survey in August 2012. The first place and more than 18 percent of votes was awarded to pop music, rock scored 13.1 percent and landed in fourth place. Interestingly, Christian music made it to top five with nine percent of votes. The younger generation is also widely represented among country music performers with such prominent names as Taylor Swift (born in 1989), who was the highest paid musician in 2015, and Hunter Hayes (born in 1991).

Country music is also able to attract crowds (and large sums of money) to live performances. Luke Bryan’s tour was the most successful tour in North America in 2016 based on ticket sales as almost 1.43 million tickets were sold for his shows. Fellow country singer, Garth Brooks, came second on the list, selling 1.4 million tickets for his tour in North America in 2016.

Taylor Swift é uma cantora estadunidense que começou sua carreira com o gênero country, mas se estabeleceu como uma cantora que trabalha com diferentes gêneros musicais. Ela é conhecida por compor suas próprias canções e em 2020, durante a pandemia de Covid-19, ela decide lançar o álbum folklore contendo 16 músicas novas. Este trabalho procura analisar duas letras de canções, “my tears ricochet” e “mad woman”, do álbum através da perspectiva interseccional da crítica feminista estadunidense. Utilizando textos de Paige L. Sweet, Stephanie Sarkis e Lourdes Maria Bandeira buscamos investigar como gaslighting está relacionado a desigualdade de gênero e a violência contra mulheres. Através dessa pesquisa, exploramos como esses assuntos são abordados nas letras das canções mencionados. Além disso, buscamos identificar como a loucura feminina é retratada na letra de canção de “mad woman”, se está ligado à rebeldia feminina como investigam Gilbert e Gubar ou as doenças mentais como estuda Donaldson, e se esse tipo de representação é ou não adequada.

The most successful music streaming service in the United States was Apple Music as of September, with the most up to date information showing that 49.5 million users accessed the platform each month. Spotify closely followed, with a similarly impressive 47.7 million monthly users.

What is a music streaming service?

Music streaming services provide their users with a database compiled of songs, playlists, albums and videos, where content can be accessed online, downloaded, shared, bookmarked and organized.

The music streaming business is huge, and has sometimes been lauded as the savior of the music industry. The biggest two services are in constant competition for the monopoly of the market. Apple Music was launched in 2015, whereas Spotify has been around since 2008. Other popular streaming services include Deezer, SoundCloud and iHeartRadio.

Do artists make a lot of money from streaming services? 

In short, unfortunately not. Both Apple Music and Spotify have been frequently criticized for the tiny royalty payments they offer artists. Particularly for emerging talent, streaming services are far from a lucrative source of income. Bigger, established stars like Taylor Swift are more likely to regularly make a good amount of money this way. But either way, a track needs to go viral or be streamed several million times before it earns any real cash.

In 2024, global music revenue generated by digital music and downloads continued its decline compared to recent years, at the level of *** billion U.S. dollars. It was the lowest figure reported since 2005. Music streaming is still in the lead The music industry has undergone fundamental changes due to the shifts in consumer demand and behavior. In 2024, global recorded music revenue hit an all-time high of around **** billion U.S. dollars, and while this figure was partially fueled by the revival of the live music sector, the top driver of growth was streaming. Music streaming accounted for an estimated ** percent of industry revenues in 2024, whereas digital downloads contributed less than ***** percent to the annual total. Audiences have come to prefer access models over ownership in recent years, which is no surprise considering the extensive range of titles available for a set rate on platforms like Spotify. Best-selling titles Most music lovers listen to their favorite tracks and discover new artists via streaming platforms. And yet, some fans also choose to pay for individual music purchases, be it via digital downloads or in physical formats. In 2023, Seventeen's “FML” was the best-selling music album worldwide, with over *** billion units sold. The K-pop band featured two of its albums in the same ranking that year. However, when it comes to vinyl, Taylor Swift led the ranks there. Her albums took up the top three spots on a list oftop-selling vinyl albums that same year.