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Automated Classification of Emotions Using Song Lyrics

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.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc177253
Date12 1900
CreatorsSchellenberg, Rajitha
ContributorsMihalcea, Rada, 1974-, Tarau, Paul, Caragea, Cornelia
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
FormatText
RightsPublic, Schellenberg, Rajitha, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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