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Automated Analysis and Emotion-based Classification of Music Lyrics

Rok Lampret (2017) Automated Analysis and Emotion-based Classification of Music Lyrics. EngD thesis.

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    In a recent study on Lyrics Music Emotion Recognition a new set of features was proposed. These new features were proved to increase accuracy of existing models for classification and regression based on valence and arousal of emotion in lyrics. Based on the findings of this study we have implemented our own system for automatically acquiring, analyzing and classifying lyrics. We only dealt with lyrics in English. First we acquired the lyrics from the web. Then we prepared the lyrics for feature extraction, using the preprocessor we implemented. A variety of functions were implemented for feature extraction, which were then used to extract features from the preprocessed lyrics. Using feature selection algorithms we ranked the features and selected only the best. Using randomized hyper-parameter optimization we optimized the parameters of learning methods for our models. For classification and regression we used two learning algorithms, Support Vector Machine and Gradient Boosting. In the end we evaluated our models with stratified 10-fold cross validation. In this work we present the methods that we used to build our system, final solution and the results that we achieved in comparison to the study we based our system on.

    Item Type: Thesis (EngD thesis)
    Keywords: emotion recognition, feature extraction, lyrics, machine learning, natural language processing
    Number of Pages: 67
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Matija Marolt271Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1537529027)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3931
    Date Deposited: 13 Sep 2017 15:53
    Last Modified: 22 Sep 2017 09:16
    URI: http://eprints.fri.uni-lj.si/id/eprint/3931

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