TY - JOUR AU - Mendhakar, Akshay AU - Tilmatine, Mesian PY - 2023/05/15 Y2 - 2024/03/29 TI - Automatic Authorship Classification for German Lyrics Using Naïve Bayes JF - Journal for Language Technology and Computational Linguistics JA - JLCL VL - 36 IS - 1 SE - Research articles DO - 10.21248/jlcl.36.2023.242 UR - https://jlcl.org/article/view/242 SP - 171-182 AB - <p>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.</p> ER -