https://github.com/dsrichard97/csulbdataday24m

Music Repo for sentiment analysis configuration
https://github.com/dsrichard97/csulbdataday24m

programming quatro r sentiment-analysis

Last synced: 7 months ago
JSON representation

Music Repo for sentiment analysis configuration

• Host: GitHub
• URL: https://github.com/dsrichard97/csulbdataday24m
• Owner: dsrichard97
• License: mit
• Created: 2024-03-22T05:38:26.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2025-01-23T09:39:59.000Z (over 1 year ago)
• Last Synced: 2025-01-23T10:31:06.405Z (over 1 year ago)
• Topics: programming, quatro, r, sentiment-analysis
• Language: HTML
• Homepage:
• Size: 11.2 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          



  



Musical Artist Sentiment Analysis




  

  

  

  

  

  



  
Authors

  


    
@dsrichard97

    
@OKcomputer626


  




  
Table of Contents

  


    
Getting Started

    
Data Source

    
Quick Glance

    
Steps

    
End Goal


  




  

    
Getting Started

    


    The focus of this project will be on Sentiment Analysi on music data(spotify).

    


  

  
Library Installation

    


    Ensure you have R and RStudio installed on your computer. RStudio provides an integrated development environment (IDE) that makes coding in R easier. Or Posit Cloud

    

  





  

    
Data Source

    


      Data was extracted using a Token API using spotify access. For further replication on the use of an Spotify API.

    

     


      For Posit Cloud : 
Click here

    


  




  

    
Quick Glance




  





  





  

    
Steps

    
Step 1: Setting Up Your Environment

Install R and RStudio. Use RStudio's IDE for ease of development.

Install required libraries with the command:


    install.packages(c("tidyverse", "tidytext", "ggwordcloud", "ggtext", "showtext", "gt"))



Step 2: Load the Libraries

Load the necessary libraries in R:


    

library(tidyverse)

library(tidytext)

library(ggwordcloud)

library(ggtext)

library(showtext)

library(gt)

    



Step 3: Getting and Preparing Your Data



    
Choose your data source, focusing on the musical artist and songs for analysis.

    
Load your data with read_csv("path/to/your/data.csv").

    
Clean your data to prepare it for analysis.



Step 4: Text Processing

Tokenize the lyrics into individual words using tidytext::unnest_tokens() and filter out common stop words.


Step 5: Sentiment Analysis

Choose a sentiment lexicon and match words to sentiments. Example:


    

bing_sentiments <- tidy_df %>%

  inner_join(get_sentiments("bing")) %>%

  count(word, sentiment, sort = TRUE)

    



Step 6: Visualization and Interpretation



    
Visualize common words with word clouds.

    
Plot frequency of sentiments with ggplot2.

    
Analyze results for trends in sentiment.





  



Step 7: Share Your Findings

Compile your analysis into a report using Quattro or R markdown. Share on Github. Use as a project for resume.