High-frequency words have higher frequencies in Turkish social sciences article

Necmi Gürsakal, Sadullah Çelik () and Serkan Özdemir ()
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Sadullah Çelik: Aydın Adnan Menderes University
Serkan Özdemir: Aydın Adnan Menderes University

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 2, No 41, 1865-1887

Abstract: Abstract Words, sentences, and paragraphs are the basis of texts. When we consider texts as data and want to establish a relationship between qualitative and quantitative perspectives, we can do this with the word frequencies in a text. We aim to examine to what extent the relative frequencies of the words differ in Turkish and English scientific articles. Using R software, 120 articles, including Turkish-Social Sciences, Turkish-Science, English-Social Sciences, and English-Science articles, were analyzed in terms of word frequencies by using a random sampling technique. The articles were analyzed based on the relative frequencies of the first 20 frequently used words. It was determined that the word frequencies in the four different categories we examined descended from top left to right down similarly. Still, the relative frequency averages of the first four words in the Turkish-Social Sciences category were very different from the other three groups. In addition, the number of words in English articles is higher than in Turkish articles in terms of average and variability higher. This situation shows that there is an excessive focusing problem in Turkish-Social Sciences articles. The results of excessive focus in Turkish-Social Sciences articles can be seen in the way that the articles tend to be focused on a single topic, and they often lack a broader perspective. This can lead to several problems, including a lack of understanding of the issues at hand, and a lack of ability to see the bigger picture. Additionally, this focus can also lead to a lack of objectivity and a lack of critical thinking.

Keywords: Text mining; High-frequency words; Turkish-scientific article; Excessive focusing; Computational linguistic (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01444-3

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