Sentiment Analysis of Music using Statistics and Machine Learning

Released on by Aakash Mukherjee
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  • Sentiment Analysis of Music using Statistics and Machine Learning Book Detail

  • Author : Aakash Mukherjee
  • Release Date : 2022-10-16
  • Publisher : Sanctum Books
  • Genre : Music
  • Pages : 78
  • ISBN 13 : 8195293174
  • File Size : 55,55 MB
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Sentiment Analysis of Music using Statistics and Machine Learning by Aakash Mukherjee PDF Summary

Book Description: Sentiment analysis and prediction of contemporary Music can have a wide range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers respectively. In this project, a music recommendation system is built upon a Naive Bayes Classifier trained to predict the sentiment of songs based on song lyrics alone. Online streaming platforms have become one of the most important forms of music consumption. Most streaming platforms provide tools to assess the popularity of a song in the forms of scores and rankings. In this book, we address two issues related to song popularity. First, we predict whether an already popular song may attract higher-than-average public interest and become viral. Second, we predict whether sudden spikes in the public interest will translate into long-term popularity growth. We base our findings on data from the streaming platform Billboard, Spotify, and consider appearances in its "Most-Popular" list as indicative of popularity, and appearances in its "Virals" list as indicative of interest growth. We approach the problem as a classification task and employ a Support Vector Machine model built on popularity information to predict interest, and vice versa.

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Music Data Mining

Music Data Mining

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The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of m