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.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc177253 |
| Date | 12 1900 |
| Creators | Schellenberg, Rajitha |
| Contributors | Mihalcea, Rada, 1974-, Tarau, Paul, Caragea, Cornelia |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Schellenberg, Rajitha, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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