NEŞET ERTAŞ TÜRKÜLERİNDE DUYGULANIM BOYUTUNUN İNCELENMESİ: BİR DUYGULAR ENDEKSİ UYGULAMASI
Authors: KÜTÜK YASİN 
Published: December 2022

This article aims to highlight the emotional characteristics of the lyrics and compositions of the folk songs sung by Neşet Ertaş. For this purpose, the lyrics of 339 songs and the musical information of 264 compositions were crawled. There is also a speech among them. Three different sources have been used to obtain the lyrics and text of speech. Besides, an additional process on to them has been applied; finally, verse-based or sentence-based texts have been obtained. In this process, these texts have been removed from certain words that do not have a meaning on their own, such as unnecessary punctuations, stopwords, onomatopoeic words, question suffixes, and transformed into a plain form. After this stage, verses or sentences have been made processable. First of all, to find the Turkish sentiments of each lyric, state-of-the-art machine learning techniques have been applied to them, and the sentiment scores of each lyric have been computed. Three different machine learning models have been used to calculate sentiment scores. These three different models are re-trained for the Turkish sentiments, which are of the models developed by Google. Only a single sentiment score has been obtained for each verse or sentence. The average of this sentiment score obtained from three different models has been calculated and used to satisfy consistency. Afterward, a sentiment vector consisting of the verse of each song or each sentence of the speech text has been created. The average of this sentiment vector is the overall score of that text (song or speech). The variance of this sentiment vector has also been calculated, in which this variance has been used to measure the sentiment tides in the song or speech text. Later, some valuable variables from a widely used music listening platform have been obtained as a basis for composition information. Here, 10 variables named acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, valence, popularity have been obtained. Three of them, danceability, energy, and valence variables have been taken into account to estimate the emotional score of the composition by getting the average of these three variables. These scores are going to be matched then with the musical information of compositions of each unique song which are 169 intersectional sets of them, and an emotional map has been drawn statistically, which can be modeled with the dimension of sentiments of lyrics and sentiments of composition showing Neşet Ertaş's emotional map. In the map of emotions, the axes have been set in the range of (-1, +1). Therefore, the scores obtained for both texts and compositions have been rescaled in the range of (-1, +1) with a formula generated for this purpose, which makes the scores of both texts and compositions are made comparable. After this stage, the sentiments of the texts are placed on the x-axis for the map of emotions while composition sentiments are placed on the y-axis. On the x-axis, which is called the lyrics scale, a score of -1 is indicated by feelings of "sorrow", and a score of +1 is indicated by feelings of "happy". The composition scale, on the other hand, shows -1 score as “melancholic” and +1 score as “cheerful” on the y-axis. According to the results obtained, it has been determined that the dominant characteristic of Neşet Ertaş's lyrics is based on sorrow words and that most of his folk songs are grouped in sorrowful lyrics and melancholic compositions. In addition, it has been found that his words are characterized by emotional changes and emotional tides; it has been also determined thanks to word cloud analysis that he often has given humane and moral advice in phrases in the lyrics. Keywords: Neşet Ertaş, Sentiment Analysis, Sentiment Index, Machine Learning, Deep Learning.