12  Text and Music Analysis

On this page, there is a comprehensive collection of resources focused on social media and music analysis. The page includes links to books, courses, and tutorials that can help with social media, music, and lyric analysis.

12.1 Text Analysis

12.1.1 Books

  1. Text Mining with R
  2. Social Media Mining
  3. Social Media Mining (Slides)
  4. Twitter sentiment analysis in R
  5. QuantEDA

12.1.2 Tutorials

  1. ANALYSING TWITTER DATA WITH R
  2. ANALYSING TWITTER DATA: EXPLORING USER PROFILES AND RELATIONSHIPS
  3. ANALYSING TWITTER DATA: EXPLORING TWEETS CONTENT
  4. 4 ways how to create a gradient word cloud in R
  5. Analyzing Google Trends with R: Retrieve and plot with gtrendsR
  6. Quantitative Analysis of Political Text
  7. Classifying Tweets for Sentiment Analysis
  8. Advancing Text Mining with R and quanteda
  9. Using Twitter With Python And Tweepy
  10. Using Twitter as a data source an overview of social media research tools (2021)
  11. Twitter “Account Analysis” in R
  12. Calculating (Twitter) Vocabulary Breadth of U.S. Presidential Candidates Using TTR
  13. Twitter data analysis in R
  14. Twitter’s Feelings About Programming Languages
  15. Look who’s tweeting: 2020 Presidential Candidates
  16. A TidyText Analysis With R
  17. Getting started with stringr for textual analysis in R
  18. Text Mining and Presidential Address
  19. Introduction to tidytext
  20. tidytext: Text mining using tidy tools
  21. Sentiment Analysis of The Office
  22. Introduction to Text Mining with R
  23. Sentiment Analysis and Text Mining with R: Word Clouds, NRC Lexicon, and Scores
  24. Sentiment Analysis with Python
  25. Advancing Text Mining with R and quanteda

12.1.3 Courses and Resources

  1. Text As Data
  2. Text Analysis: Text Analysis Using R

12.1.4 Projects

  1. twitter-sentiment-analysis
  2. Analytics Master’s Research Project

12.2 Music and Lyric Analysis

12.2.1 Basics

  1. Spotify R&D
  2. spotifyr package
  3. 13 best websites to analyze your Spotify data

12.2.2 Tutorials

  1. Spotify Songs: Tidy Tuesday
  2. How to integrate Spotify and Genius API to easily crawl song lyrics with Python
  3. Profiling Songs on Spotify Using Cluster Analysis
  4. Sentiment Analysis of Most Streamed Spotify Artists
  5. Lyric Analysis with NLP & Machine Learning with R
  6. What’s popping?: An Exploratory Analytics Project on What Makes Popular Music Popular
  7. Analysis of my Spotify in R with spotifyr package
  8. Game of Thrones: Exploratory and Sentiment Analysis
  9. tayloR
  10. Exploring the Spotify API with R
  11. Learn tidytext with my new learnr course
  12. Text mining with tidy data principles
  13. Are Pop Lyrics Getting More Repetitive?
  14. Spotify Songs: An analysis of the spotify dataset
  15. FitteR HappieR
  16. Data Wrangling - Spotify Dataset Analysis
  17. Understanding + classifying genres using Spotify audio features
  18. What Makes a Hit
  19. What Makes Songs Popular? It’s All About ‘You’
  20. Can big data really predict what makes a song popular?
  21. PCA and the #TidyTuesday best hip hop songs ever
  22. Blue Christmas: A data-driven search for the most depressing Christmas song
  23. Using Data to Find the Angriest Death Grips Song
  24. Spotify songs popularity analysis
  25. Nigerian Songs Spotify EDA
  26. Sentiment Analysis of Spotify, Amazon Music, and Pandora user reviews
  27. How to download YouTube data in R using “tuber” and “purrr”
  28. How to download song lyrics from Genius using Python