Covid-19 Kısıtlamaları Sırasında Duyguların Müzikal Parametrelerle İfade Edilmesi: Filipinler Spotify Verileri Üzerine Bir Duygu Analizi

Öz

Spotify, dinleyicilerin dinleme kalıplarına göre çok çeşitli çalma listeleri sunan dünyanın en büyük çevrimiçi müzik paylaşım platformudur. Bu makale, Filipinler verisi üzerine yapılan incelere göre, müzik tercihinin duygusal durumla yüksek oranda ilişkili olduğunu ve müziğin pandemi sırasında bir duygu düzenleyici araç olduğunu ortaya koymaktadır. Popüler makine öğrenimi yöntemleri (sınıflandırma ve regresyon ağaçları, güçlendirilmiş ağaçlar, rastgele ormanlar, Destek Vektör Makineleri ve Yapay Sinir Ağları), pandeminin şiddetiyle orantılı olarak farklı zaman periyotlarında insanların müzik tercihlerini sınıflandırmak için, 5 katlı çapraz doğrulama ile birlikte kullanılmıştır. Algoritmaların öğrenme süreçlerinde günlük resmi covid-19 istatistikleri ve Spotify verileri ana değişkenler olarak kullanılmaktadır. SVM, %98,01 doğruluk oranı elde ederek ortalama doğruluk oranında diğer alternatif modeller içinde en iyi performansı göstermiştir. Ek olarak, ANN, özellikle tek bir modelde elde edilen doğruluk açısından diğer alternatiflerden daha iyi performans göstererek %99,30 doğruluk oranına ulaşmıştır. Ayrıca (azalan sırada) en büyük (mutlak) değişime sahip değişkenler ST_Intrumentalness (%26,45), ST_Acousticness (%19,03), ST_Liveness (%16,11) ve ST_Valence (%14,1)'dir. Pandemi kaynaklı stres ve konserlerin iptali göz önüne alındığında, ST_Valence (müzikal pozitiflik) ve ST_Liveness değişkenlerinin böyle bir oranda değişeceği sezgisel bir beklenti olmuştur. Sonuçlar, müzik tercihinin duygusal durumun önemli bir göstergesi olduğunu doğrulamaktadır.

Anahtar Kelimeler

• Nicel Karar Yöntemleri
• Veri Analitiği
• Makine Öğrenimi
• Duygu Analizi
• Müzik

The Expression of Emotions Through Musical Parameters During the Covid-19 Restrictions: A Sentiment Analysis on Philippines Spotify Data

Öz

Spotify is the world's largest online music streaming platform that offers a tremendous variety of playlists based on listeners' listening patterns. This paper proposes that music preference is highly associated with emotional state, and music is an emotion regulator tool during the pandemic in the Philippines. Well-known machine learning methods (i.e., classification and regression trees, boosted trees, random forests, Support Vector Machines, and Artificial Neural Networks) in combination with 5-fold cross-validation are used to classify periods in proportion to the severity of the pandemic and people's musical preferences. Daily official covid-19 statistics and Spotify data are used as main variables during the algorithms' learning processes. SVM outperformed the other alternatives in average accuracy rate by achieving a 98.01% accuracy rate. Additionally, ANN outperformed the other alternatives in terms of accuracy achieved specifically in a single model, achieving an accuracy rate of 99.30%. Moreover, the variables with the largest (absolute) change (in descending order) are ST_Intrumentalness (26,45%), ST_Acousticness (19,03%), ST_Liveness (16,11%), and ST_Valence (14,1%). Given pandemics-related stress and cancelation of concerts, it would be an intuitive expectation that the variables ST_Valence (musical positivity) and ST_Liveness would change at such a rate. The results confirm that musical preference is a significant indicator of emotional state.

Anahtar Kelimeler

• Quantitative Decision Methods
• Data Analytics
• Machine Learning
• Sentiment Analysis
• Music

Kaynakça

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