Abstract

This thesis explores the possibility of predicting the mood a song will evoke in a person based on certain musical properties that the song exhibits. First, I introduce the topic of data mining and establish its significant relevance in this day and age. Next, I explore the several tasks that data mining can accomplish, and I identify classification and clustering as the two most relevant tasks for mood prediction based on musical properties of songs. Chapter 3 introduces in detail two specific classification techniques: Naive Bayes Classification and k-Nearest Neighbor Classification. Similarly, Chapter 4 introduces two specific clustering techniques: k-Means Clustering and k-Modes Clustering. Next, Chapter 5 implements these previously discussed classification and clustering techniques on a data set involving musical property combinations and mood, and makes conclusions about which musical property combinations will most likely evoke a happy, sad, calm, or agitated mood. It can be concluded very generally that songs that are in major keys, have consonant harmonies, faster tempos, and firm rhythms will most likely evoke a happy mood in a person. Songs that are in minor keys, have slower tempos, and firm rhythms will probably evoke a sad mood in a person. Songs that are in major keys and have flowing rhythms tend to evoke a calm mood in a person. And last but not least, songs that are in minor keys, have dissonant harmonies, and firm rhythms will evoke an agitated mood in a person.

Advisor

Hartman, James

Department

Mathematics

Disciplines

Applied Mathematics

Publication Date

2013

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis Exemplar

Additional Files
I.S.songs.m4a (9548 kB)
Sample songs used to predict mood. Mp3 format
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© Copyright 2013 Sarah Smith-Polderman