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L,M&A: An Algorithm for Music Lyrics Mining and Sentiment Analysis

9 pagesPublished: March 13, 2019


Here we propose an open source algorithm, L,M&A(Lyrics, Mine and Analyse) to create a dataset of lyrics of the works of various artists. The aim of this approach is to facilitate the generation of a large data set that can be used for improving accuracy of song recommendation algorithms. The limited availability of such datasets has excluded the sentiment analysis of lyrics from music recommendation systems. By using the L,M&A algorithm, it is possible to generate a large dataset which can function as training dataset for future classifier systems. We have used iterative API requests from musixmatch and Genius servers to text mine lyrics data of songs by multiple artists. The data is processed and then analysed for sentiment using lexicons provided in the Tidytext package (BING, AFINN, NRC) and the overall sentiment of artist was determined through modal counts. The occurrence of each sentiments was evaluated and visualized using ggplot2. This representation exhibits the merit of our approach and the applicability of our data. The key feature of our approach is the open source platforms utilized and simplicity of input required from user.

Keyphrases: Data Mining, Genius, Lyrics Database, music recommendation, musicology, MusiXmatch, Natural Language Processing, RStudio, Sentiment Analysis

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 475--483

BibTeX entry
  author    = {Vasu Saluja and Minni Jain and Prakarsh Yadav},
  title     = {L,M\textbackslash{}\&A: An Algorithm for Music Lyrics Mining and Sentiment Analysis},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  pages     = {475--483},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/wkj6}}
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