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    <titleInfo>
        <title>Automatic Authorship Classification for German Lyrics Using Naïve Bayes</title>
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    <name type="personal">
        <namePart type="given">Akshay</namePart>
        <namePart type="family">Mendhakar</namePart>
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    <name type="personal">
        <namePart type="given">Mesian</namePart>
        <namePart type="family">Tilmatine</namePart>
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            <title>Journal for Language Technology and Computational Linguistics, Vol. 36 No. 1</title>
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            <namePart type="given">Roman</namePart>
            <namePart type="family">Schneider</namePart>
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    <abstract>Text classification is a prevalent and essential machine-learning task. Machine learning classifiers have developed immensely since their inception. The naïve Bayes classifier is one of the most prominent supervised machine learning classifiers. In this experiment, we highlight the performance of Naïve Bayes for classifying of authors/artists on the German lyrics corpus (“Songkorpus”) and compare the classification results with other classifier algorithms. The corpus of investigation consists of six artists with 970 songs in total. Bayes model evaluation measures revealed a precision of 0.91, recall of 0.94, and F1-measure of 0.9. Furthermore, the classification performance with other classifier algorithms did not reveal any statistically significant difference in performance. The results of the study add to the high volume of reports on the classification accuracy of Naive Bayes for the task of lyrical classification.</abstract>
    <identifier type="citekey">mendhakar-tilmatine-2023-automatic</identifier>
    <identifier type="doi">10.21248/jlcl.36.2023.242</identifier>
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        <url>https://aclanthology.org/2023.jlcl-1.9</url>
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        <date>2023-05</date>
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            <start>171</start>
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%0 Conference Proceedings
%T Automatic Authorship Classification for German Lyrics Using Naïve Bayes
%A Mendhakar, Akshay
%A Tilmatine, Mesian
%Y Schneider, Roman
%Y Gertrud, Faaß
%S Journal for Language Technology and Computational Linguistics, Vol. 36 No. 1
%D 2023
%8 May
%I German Society for Computational Lingustics and Language Technology
%C unknown
%F mendhakar-tilmatine-2023-automatic
%X Text classification is a prevalent and essential machine-learning task. Machine learning classifiers have developed immensely since their inception. The naïve Bayes classifier is one of the most prominent supervised machine learning classifiers. In this experiment, we highlight the performance of Naïve Bayes for classifying of authors/artists on the German lyrics corpus (“Songkorpus”) and compare the classification results with other classifier algorithms. The corpus of investigation consists of six artists with 970 songs in total. Bayes model evaluation measures revealed a precision of 0.91, recall of 0.94, and F1-measure of 0.9. Furthermore, the classification performance with other classifier algorithms did not reveal any statistically significant difference in performance. The results of the study add to the high volume of reports on the classification accuracy of Naive Bayes for the task of lyrical classification.
%R 10.21248/jlcl.36.2023.242
%U https://aclanthology.org/2023.jlcl-1.9
%U https://doi.org/10.21248/jlcl.36.2023.242
%P 171-182

Markdown (Informal)

[Automatic Authorship Classification for German Lyrics Using Naïve Bayes](https://aclanthology.org/2023.jlcl-1.9) (Mendhakar & Tilmatine, JLCL 2023)

• Automatic Authorship Classification for German Lyrics Using Naïve Bayes (Mendhakar & Tilmatine, JLCL 2023)

ACL

• Akshay Mendhakar and Mesian Tilmatine. 2023. Automatic Authorship Classification for German Lyrics Using Naïve Bayes. In Journal for Language Technology and Computational Linguistics, Vol. 36 No. 1, pages 171–182, unknown. German Society for Computational Lingustics and Language Technology.