MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics
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In this talk, I present a machine learning approach to build a music recommendation system that can identify happy songs in a music library based on song lyrics. Also, this presentation covers a general introduction to predictive modeling, naive Bayes classification, and text classification.
![Music Mood Prediction
• We like to listen to music [1][2]
• Digital music libraries are growing
• Recommendation system for happy music
(clinics, restaurants ...) & genre selection
[1] Thomas Schaefer, Peter Sedlmeier, Christine Sta ̈dtler, and David Huron. The psychological functions of music listening. Frontiers in psychology, 4, 2013.
[2] Daniel Vaestfjaell. Emotion induction through music: A review of the musical mood induction procedure. Musicae Scientiae, 5(1 suppl):173–211, 2002.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics
- 1. MusicMood Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics Sebastian Raschka December 10, 2014
- 2. Music Mood Prediction • We like to listen to music [1][2] • Digital music libraries are growing • Recommendation system for happy music (clinics, restaurants ...) & genre selection [1] Thomas Schaefer, Peter Sedlmeier, Christine Sta ̈dtler, and David Huron. The psychological functions of music listening. Frontiers in psychology, 4, 2013. [2] Daniel Vaestfjaell. Emotion induction through music: A review of the musical mood induction procedure. Musicae Scientiae, 5(1 suppl):173–211, 2002.
- 3. Predictive Modeling Unsupervised learning Supervised learning Clustering Classification Regression Reinforcement learning Hidden Ranking Markov models DBSCAN on a toy dataset Naive Bayes on Iris (after LDA)
- 4. Supervised Learning In a Nutshell
- 5. Feature Extraction Feature Selection Normalization Dimensionality Reduction Raw Data Collection Pre-processing Sampling Supervised Learning - A Quick Overview Pre-processing Test Dataset New Data Training Dataset Training Learning Algorithms Post-Processing Cross Validation Final Classification/ Regression Model Refinement Prediction Split Supervised Learning Sebastian Raschka 2014 Missing Data Prediction-error Metrics Model Selection Hyperparameter optimization This work is licensed under a Creative Commons Attribution 4.0 International License.
- 6. MusicMood - The Plan
- 8. Sampling Lyrics available? Lyrics in English? 1000 songs for training 200 songs for validation http://lyrics.wikia.com/Lyrics_Wiki Python NLTK
- 9. Mood Labels Downloading mood labels from Last.fm Manual labeling based on lyrics and listening sad if ... • Dark topic (killing, war, complaints about politics, ...) • Artist in sorrow (lost love, ...)
- 11. Word Clouds happy: sad:
- 12. A Short Introduction to Naive Bayes Classification
- 13. Naive Bayes - Why? • Small sample size, can outperform the more powerful alternatives [1] • "Eager learner" (on-line learning vs. batch learning) • Fast for classification and re-training • Success in Spam Filtering [2] • High accuracy for predicting positive and negative classes in a sentiment analysis of Twitter data [3] [1] Pedro Domingos and Michael Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine learning, 29(2-3):103–130, 1997. [2] Mehran Sahami, Susan Dumais, David Heckerman, and Eric Horvitz. A bayesian approach to filtering junk e-mail. In Learning for Text Categorization: Papers from the 1998 workshop, volume 62, pages 98–105, 1998. [3] Alec Go, Richa Bhayani, and Lei Huang. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, pages 1–12, 2009.
- 14. Bayes Classifiers It’s All About Posterior Probabilities objective function: maximize the posterior probability
- 15. The Prior Probability Maximum Likelihood Estimate (MLE)
- 16. The Effect of Priors on the Decision Boundary
- 17. Class-Conditional Probability Maximum Likelihood Estimate (MLE) "chance of observing feature given that it belongs to class " "
- 18. Evidence just a normalization factor, can be omitted in decision rule:
- 19. Naive Bayes Models Gaussian Naive Bayes for continuous variables
- 20. Naive Bayes Models Multi-variate Bernoulli Naive Bayes for binary features
- 21. Naive Bayes Models Multinomial Naive Bayes
- 22. Naive Bayes and Text Classification
- 23. Feature Vectors The Bag of Words Model
- 24. Tokenization and N-grams “a swimmer likes swimming thus he swims”
- 25. Stemming and Lemmatization Porter Stemming Lemmatization
- 28. Term Frequency - Inverse Document Frequency (Tf-idf)
- 29. Grid Search and 10-fold Cross Validation to Optimize F1 • TP = true positive (happy predicted as happy) • FP = false positive (sad predicted as happy) • FN = false negative (happy predicted as sad)
- 31. 10-Fold Cross Validation After Grid Search (final model)
- 32. 10-fold Cross Validation (mean ROC) Multinomial vs Multi-variate Bernoulli Naive Bayes
- 33. 10-fold Cross Validation (mean ROC) Multinomial Naive Bayes & Hyperparameter Alpha
- 34. 10-fold Cross Validation (mean ROC) Multinomial Naive Bayes & Vocabulary Size
- 35. 10-fold Cross Validation (mean ROC) Multinomial Naive Bayes & Document Frequency Cut-off
- 36. 10-fold Cross Validation (mean ROC) Multinomial Naive Bayes & N-gram Sequence Length
- 37. Contingency Tables of the Final Model training test
- 38. Live Demo http://sebastianraschka.com/ Webapps/musicmood.html
- 39. Future Plans • Growing a list of mood labels (majority rule). • Performance comparisons of different machine learning algorithms. • Genre prediction and selection based on sound.
- 40. Thank you!