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- W612026192 abstract "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." @default.
- W612026192 created "2016-06-24" @default.
- W612026192 creator A5090938070 @default.
- W612026192 date "2012-12-01" @default.
- W612026192 modified "2023-09-26" @default.
- W612026192 title "Automated Classification of Emotions Using Song Lyrics" @default.
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