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

Primarily a statistician, Soubhik Chakraborty is currently a Professor and formerly Head in the department of Mathematics, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. His research interests are algorithm and music analysis involving extensive use of computational statistics. He has been guiding several research scholars leading to PhD in these areas and has published over 100 papers, 4 books and 7 research monographs. He is also an acknowledged reviewer associated with ACM, IEEE and AMS. Himself an amateur harmonium player and a former flutist, his recently published book Computational Musicology in Hindustani Music, joint with Prof. Guerino Mazzola (School of Music, University of Minnesota, USA) and Chakraborty’s own PhD scholars Dr Swarima Tewari and Dr Moujhuri Patra published by Springer in 2014 is the first book dedicated to the topic. He is also one of the authors of the book Signal Analysis of Hindustani Classical Music, Springer, 2017 jointly authored by Prof. Asoke Kumar Datta of Indian Statistical Institute, Kolkata and four other co-authors.

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