Automated Classification of Emotions Using Song Lyrics

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Description

This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics. I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the ... continued below

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Schellenberg, Rajitha December 2012.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 826 times , with 16 in the last month . More information about this thesis can be viewed below.

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  • Schellenberg, Rajitha

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Description

This thesis explores the classification of emotions in song lyrics, using automatic approaches applied to a novel corpus of 100 popular songs. I use crowd sourcing via Amazon Mechanical Turk to collect line-level emotions annotations for this collection of song lyrics.  I then build classifiers that rely on textual features to automatically identify the presence of one or more of the following six Ekman emotions: anger, disgust, fear, joy, sadness and surprise. I compare different classification systems and evaluate the performance of the automatic systems against the manual annotations. I also introduce a system that uses data collected from the social network Twitter. I use the Twitter API to collect a large corpus of tweets manually labeled by their authors for one of the six emotions of interest. I then compare the classification of emotions obtained when training on data automatically collected from Twitter versus data obtained through crowd sourced annotations.

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UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

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  • December 2012

Added to The UNT Digital Library

  • Aug. 13, 2013, 2:47 p.m.

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  • Nov. 16, 2016, 5:27 p.m.

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Citations, Rights, Re-Use

Schellenberg, Rajitha. Automated Classification of Emotions Using Song Lyrics, thesis, December 2012; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc177253/: accessed October 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .