Türkçe şarkılar için şarkı sözleri üzerinden müzik duygu sınıflandırması

Yıl 2018, Cilt: 24 Sayı: 2, 292 - 301, 30.04.2018

Ahmet Onur Durahim , Abide Coşkun Setirek Birgül Başarır Özel Hanife Kebapçı

Öz

Müzik insanlık tarihinde önemli bir yere
sahiptir. Özellikle dijital çağda kişiler tarafından her gün yaratılan ve
ulaşılan müzik koleksiyonlarının büyüklüğü ile müziğin önemi daha da artmış ve
insanlar müzik içeren aktivitelere daha fazla zaman ayırmaya
başlamışlardır. Bununla birlikte, müziğe bilgi geri getirim
sürecini kolay ve etkin hale getirmek için yapılan katalog bazlı aramalar duygu
tabanlı etiketlere göre aramalara dönüşmüştür. Bu araştırmada amacımız şarkı
sözlerine göre bir şarkıdan algılanan duygunun otomatik olarak çıkarıldığı bir
model geliştirmektir. Model metin bazlı sınıflandırma için kullanılan makina
öğrenmesi algoritmaları ile oluşturulmuştur. Bu amaçla araştırmada 300 şarkı
seçilmiş ve bu şarkılar kişiler tarafından hissedilen duygularına göre
etiketlenmiştir. Devamında metin ön analizi ile şarkı sözleri Türkçe köklerine
ayrıştırılarak Unigram, Bigram ve Trigram kelime özellikleri çıkartılmıştır.
Ardından endeksleri terim sıklığı ve tf-idf değerleri olan doküman bazında
terim matrisleri yaratılmıştır. Bu matris değerleri 5 farklı sınıflandırma
algoritmasına girdi olarak verilerek en yüksek doğruluk sonuçları, hatırlama ve
kesinlik metrikleri üzerinden araştırılmıştır. Araştırmanın sonucunda en yüksek
kesinlik değeri Zemberek Uzun Kök Ayıştırma Metodu ile Unigram kelime
özelliklerine göre ayrıştırılmış ve endeksi terim sıklığına göre belirlenmiş
terim bazlı doküman matrisinin Katlıterim Naïve Bayes kümeleyicisinde verdiği
görülmüştür. Bu kombinasyonda hatırlama metriği değeri 43.7 iken kesinlik
metriği değeri 46.9’dur.

Anahtar Kelimeler

Metin madenciliği, Metin sınıflandırması, Duygu analizi, Müzik duygu geri getirim

Music emotion classification for Turkish songs using lyrics

Öz

Music
has grown into an important part of people’s daily lives. As we move further
into the digital age in which a large collection of music is being created
daily and becomes easily accessible renders people to spend more time on
activities that involve music. Consequently, the form of music retrieval is
changed from catalogue based searches to searches made based on emotion tags in
order for easy and effective musical information access. In this study, it is
aimed to generate a model for automatic recognition of the perceived emotion of
songs with the help of their lyrics and machine learning algorithms. For this
purpose, first 300 songs are selected and annotated by human taggers with
respect to their perceived emotions. Thereafter, Unigram, Bigram and Trigram
word features are extracted from song lyrics after performing text
preprocessing where stemming of the Turkish words is an essential part. Then,
term by document matrices are created where term frequencies and tf-idf scores
are considered as representations for the indices. Five different
classification algorithms are fed with these matrices in order to find the best
combination that achieves the highest accuracy results where recall and
precision values are used as comparison metrics. As a result, best accuracy
results are obtained by using Multinomial Naïve Bayes classifier where Unigram
features are used to create the term by document matrix. In this setting,
Unigram features are stemmed by Zemberek Long stemming method, and the index
representation is chosen as term frequency. For this combination, obtained
recall and precision values are 43.7 and 46.9, respectively.

Anahtar Kelimeler

Text mining, Text classification, Sentiment analysis, Music emotion retrieval

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