Using Quanteda, IBM Tone Analyzer and ggplot2 to analyse top 100 song lyrics in the US from 1958.
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Uses lyrics from Billboard Top 100 songs from 1958 in the US to do sentiment and lexical diversity analysis. Aims to determine if/how song lyrics have changed over time. Lexical diversity analysis is conducted using the Quanteda package, with the TTR and Maas measures.
Sentiment analysis run using IBM's tone analyser.
Songs appear to have become less joyful, angrier and simpler.
To analyse / play about with the data yourself, please check out the Data Studio dashboard.
Make sure you've done this setup before you try to run the code yourself.
Install Quanteda packages in R environment.
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R
install.packages("quanteda") install.packages("quanteda.textstats") install.packages("quanteda.textmodels")
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IBM Cloud Account Create a free IBM Cloud Account and use the $200 USD credit they give you to create a Tone Analyzer instance, if you want to run the sentiment analysis yourself. IBM Tone Analyzer
- Genre-level analysis
- Analysis of more songs, top 100 in other countries
- Country-level comparison
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
LinkedIn- Louis Magowan
Project Link: https://github.com/louismagowan/lyrics_analysis