Dalırnaghadeh, Donya

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Dalırnaghadeh, Donya
DALIRNAGHADEH Donya
Donya Dalirnaghadeh
Dalirnaghadeh, D.
Donya DALiRNAGHADEH
Dalirnaghadeh Donya
D., Dalırnaghadeh
Donya Dalırnaghadeh
Dalirnaghadeh, Donya
Donya DALIRNAGHADEH
DALiRNAGHADEH Donya
Donya, Dalırnaghadeh
Dalırnaghadeh Donya
Dalırnaghadeh, D.
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Dr.Öğr.Üyesi
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donya.dalirnaghadeh@okan.edu.tr
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    Article
    Citation Count: 1
    A principal component model to identify Turkish soundscapes' affective attributes based on a Corpus-Driven Approach
    (Elsevier Sci Ltd, 2023) Yilmazer, Semiha; Fasllija, Ela; Alimadhi, Enkela; Sahin, Zekiye; Mercan, Elif; Dalirnaghadeh, Donya; Mimarlık / Architecture
    This study focused on achieving linguistic and culturally appropriate equivalents of Turkish soundscape attributes present in ISO 12913-3 by incorporating a Corpus-Driven Approach (CDA). A two-phase exper-iment was set up to find Turkish equivalents of affective quality attributes. The first phase consisted of the formation of a Corpus. An online questionnaire was prepared and sent to 196 native Turkish speakers from all around Turkiye to define adjectives. The second phase of the experiment was performed in a lis-tening room. For this purpose, twenty-four binaural sound recordings were collected from seven public spaces. Afterward, forty individuals evaluated the recordings by using the attributes from Phase 1. The perceptual dimensions were obtained from the generated corpus in Turkish based on a rating scale by applying the Principal Component Analysis (PCA). Results indicated a two-dimensional model with two main components, Pleasantness and Eventfulness. Each component is associated with a main orthog-onal axis denoted by 'annoying-comfortable' and 'dynamic-uneventful,' respectively. This circular orga-nization of soundscape attributes is supported by two derived axes, namely 'chaotic-calm' and 'monotonous-enjoyable', rotated 45 degrees on the same plane. Additionally, by using Spearman's rank correla-tion coefficient, sixty-four different bipolar adjective pairs were found. The adjective pairs showed that the highest correlations are mainly on the pleasant-unpleasant continuum, namely Component 1 of PCA. The collected data were also analyzed using Agglomerative Hierarchical Cluster analysis with the Ward method in R programming language to cluster the adjectives. The results inferred that there are four top-level categories. From the first to the fourth level, categories consisted of pleasant, uneventful, eventful, and annoying adjectives, respectively. Moreover, the terms grouped on the first cluster found their dichotomous on the fourth cluster, while maintaining the same relationship in the pleasant -unpleasant continuum.(c) 2023 Elsevier Ltd. All rights reserved.