MooDetecTR: Kelime Vektörleri Vasıtasıyla Türkçe Şarkı Sözleri için Ruh Hali Tespiti

Çevrimiçi müzik platformlarının kullanımının artmasıyla birlikte, katalog tabanlı aramalar, duygu bazlı aramalara dönüşmüştür. Bu çalışmada, Türkçe şarkıların duygu durum tespiti için kelime vektörlerini kullanan yarı denetimli bir öğrenme çerçevesi olan MooDetecTR önerilmiştir. Bu çerçevede, önce kelime vektörleri Word2Vec ve GloVe algoritmaları ile 2,5 milyondan fazla Türkçe belge içeren geniş bir metinsel veri koleksiyonu kullanılarak oluşturulmuştur. Daha sonra, duygu durum tespiti için seçilen şarkı sözlerindeki kelimelerin, daha önceden eğitilmiş kelime vektörlerinin birleştirilmesiyle şarkı sözleri vektörleri üretilmiştir. Son olarak, oluşturulan bu şarkı sözleri vektörleri, müzik duygu durum tespitinde kullanılmak üzere çeşitli makine öğrenmesi algoritmaları kullanılarak oluşturulan modelleri eğitmek için kullanılmıştır. Türkçe müziklerde duygu durumu tespiti karşılaştırma yapılmak üzere ayrıca, hem TFIDF ağırlıkları kullanılarak geleneksel kelime çantası modeli ile hem de Doc2Vec algoritması kullanılarak oluşturulan modeller ile gerçekleştirilmiştir. Kelimelerin köklerine ayrıştırılması ve gereksiz kelimelerin kaldırılmasının sonuçlara etkileri de incelenmiştir. Önerilen çerçeve ile elde edilen en iyi mikro-f1 skoru (%54,36), Doc2Vec ve kelime çantası yöntemlerinden elde edilen en iyi skorlardan sırasıyla %3,81 ve %2,92 (%7,54 ve %5,68 nispi iyileştirmeler) daha başarılıdır. Sonuç olarak, elde edilen skorlar, Türkçe metin sınıflandırma uygulamasında büyük metinsel verilerin kullanılması ile oluşturulan kelime vektörlerinin olumlu etkisini artan sınıflandırma başarı performansı ile açıkça göstermektedir.

MOODETECTR: MOOD DETECTION FOR TURKISH LYRICS THROUGH WORD VECTORS

Along with the increasing use of online music platforms, catalogue-based searches haveturned into mood-based seeking. In this study, we propose MooDetecTR, a semi-supervised learningframework that employs word vectors for Turkish song mood detection. In this framework, first wordvectors are created through a large collection of textual data, which include more than 2.5 million Turkishdocuments, by using Word2Vec and GloVe algorithms. Subsequently, lyrics vectors are generatedthrough combining already trained word vectors of the words in the lyrics selected for mood detection.Lastly, lyrics vectors are fed into various machine-learning algorithms as features to create models formusic mood detection. For comparison, Turkish music mood detection is performed both via traditionalbag-of-words model, with TF-IDF weights, and Doc2Vec algorithm. The effects of stemming of the wordsand stop-words removal on the results are investigated, as well. The best micro-f1 score (54.36%) obtainedby the proposed framework is 3.81%, and 2.92% higher (7.54%, and 5.68% relative improvements) thanthe best score obtained from Doc2Vec and bag-of-words methods, respectively. Consequently, the resultsobtained show the effectiveness of incorporating word vectors generated using big textual data intoTurkish text classification process, which is clearly illustrated by the improved classification performance.

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