Sentiment Analysis of Music using Statistics and Machine Learning by Aakash Mukherjee and Soubhik Chakraborty

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Front Cover

Back Cover

Sentiment Analysis of Music using Statistics and Machine Learning

Authors:

Aakash Mukherjee, Soubhik Chakraborty

ISBN:

9788195293179

Binding:

Hardcover

Year:

2023

Pages:

84 with numerous colour and b/w figures, tables, and photos

Size:

15 x 23 x 1 cm

Weight:

257 grams

Price:

INR~~595~~536.00

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About the Book

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.

About the Authors

Aakash Mukherjee

Aakash Mukherjee has completed his Integrated M.Sc. in Mathematics & Computing from Department of Mathematics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand. His research interests are machine learning, applied and computational statistics. The present work is a part of his master’s dissertation which he completed under the guidance of Prof. Soubhik Chakraborty. Aakash Mukherjee is currently working as Data Scientist at Sumeru Inc.

Soubhik Chakraborty

Prof. (Dr) Soubhik Chakraborty, primarily a statistician, is currently a professor and former Head of the Department of Mathematics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. His research interests are algorithm analysis, music analysis and statistical computing. He has guided several research scholars in these areas to Ph.D. degrees and has authored many books, research monographs and papers in peer-reviewed journals. His book Computational Musicology in Hindustani Music, published by Springer in 2014, was described by Springer as its first book devoted to the subject.

Other books on music research that he has authored or co-authored include Music and Medicine: Healing Brain Injury Through Rāgas, CBH Pub, 2016 (with audio CD); Signal Analysis of Hindustani Classical Music, Springer, 2017; Hindustani Classical Music: A Historical and Computational Study, Sanctum Books, 2021 (winner of the IMTA award for the best book on rāga music, given by the Indian Music Therapy Association (IMTA) in 2022); and Stress Management Through Music: A Statistical Study, Notion Press, 2024 (winner of the IMTA-NADA award for the best book of 2024 for “outstanding contribution to music therapy”).

He has been the principal investigator of two music-research projects: Analysing the Structure and Performance of Hindustani Classical Music Through Statistics, sponsored by the University Grants Commission, and Hindustani Rāga Analysis Using Statistical Musicology with Therapeutic Applications for Stress Management, sponsored by IDEAS: Technology Innovation Hub @ Indian Statistical Institute, Kolkata. Two patents arising from the second project have recently been published. He is a life member of the Indian Statistical Institute and the Acoustical Society of India and is also an acknowledged reviewer associated with ACM, IEEE and AMS. He has received several awards in both teaching and research. He is also a former flautist.

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