MusicMood: Predicting the mood of music from song lyrics using machine learning

Sentiment 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, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

[1]  Markus Schedl,et al.  Mining microblogs to infer music artist similarity and cultural listening patterns , 2012, WWW.

[2]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[3]  Zellig S. Harris,et al.  Distributional Structure , 1954 .

[4]  Mitsunori Ogihara,et al.  Genre classification for million song dataset using confidence-based classifiers combination , 2012, SIGIR '12.

[5]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[6]  Peter Sedlmeier,et al.  The psychological functions of music listening , 2013, Front. Psychol..

[7]  Xavier Serra,et al.  Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques , 2000, ISMIR.

[8]  Steven Bird,et al.  NLTK: The Natural Language Toolkit , 2002, ACL.

[9]  Muhammad Rafi,et al.  Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment , 2011, 2011 IEEE 14th International Multitopic Conference.

[10]  D. Västfjäll.  Emotion induction through music: A review of the musical mood induction procedure , 2001 .

[11]  van Erp,et al.  Automatic Mood Classification for Music , 2009 .

[12]  Menno van Zaanen,et al.  Automatic Mood Classification Using TF*IDF Based on Lyrics , 2010, ISMIR.

[13]  Youngmoo E. Kim,et al.  Modeling Musical Emotion Dynamics with Conditional Random Fields , 2011, ISMIR.

[14]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.

[15]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[16]  Tao Li,et al.  Music artist style identification by semi-supervised learning from both lyrics and content , 2004, MULTIMEDIA '04.

[17]  Lie Lu,et al.  Automatic mood detection and tracking of music audio signals , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[19]  Pedro J. Moreno,et al.  A Study of Musical Instrument Classification Using Gaussian Mixture Models and Support Vector Machines , 1999 .

[20]  Tao Li,et al.  A comparative study on content-based music genre classification , 2003, SIGIR.

[21]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[22]  Susan T. Dumais,et al.  A Bayesian Approach to Filtering Junk E-Mail , 1998, AAAI 1998.