Metin madenciliği ile Türkiye’nin yabancı basındaki imajının analizi

Yıl 2025, Cilt: 27 Sayı: 2, 569 - 586, 15.07.2025

Murat Işık , Emrah Aydemir

https://doi.org/10.25092/baunfbed.1596321

Öz

Son yıllarda, birçok gazete ve haber sağlayıcısı içeriklerini web sayfaları ve sosyal medya aracılığıyla sunmaya başlamıştır. Bu dönüşüm, haber içeriklerinin hacminde büyük bir artışa yol açarak bu geniş bilgi akışının analiz edilmesi ve yönetilmesini zorunlu hale getirmiştir. Bu çalışma kapsamında, Fox, The Guardian, BBC ve CNN gibi büyük yabancı haber sağlayıcılarının web sayfalarından Türkiye ile ilgili 8.385 haber içeriği toplanmıştır. Geleneksel teknikler, haber metinlerini içeriklerine göre sınıflandırırken, bu çalışmada içerikler önceden tanımlanmış ilgi alanlarına göre sınıflandırılarak %89,36’lık ortalama bir doğruluk oranı elde edilmiştir. Ayrıca, yabancı haber içeriklerinin yayımlanma tarihleri temelinde yapılan analizler, yayımlanma tarihleri ile sınıflandırılmış ilgi alanları arasındaki ilişkiler ortaya çıkarılmıştır. Bunun yanı sıra, BERT algoritması kullanılarak toplanan haber içeriklerinde duygu analizi gerçekleştirilmiş, içeriklerin duygu kategorileri belirlenmiş ve Türkiye’nin yabancı basındaki algısı incelenmiştir.

Anahtar Kelimeler

duygu analizi , BERT , metin madenciliği , veri madenciliği , haber analizi , haber sınıflandırması , haber tarihleri ve içerik ilişkisi

Classification of news about Turkey in the foreign press through text mining

Öz

In recent years, many newspapers and news providers have begun presenting their content via web pages or through social media. This shift has led to a massive increase in the volume of news content available, necessitating the analysis and management of this vast information flow. In this study, 8,385 pieces of news content related to Turkey were collected from the web pages of major foreign news content providers, including Fox, The Guardian, BBC, and CNN. While traditional techniques classify news texts into categories based on their content, this study achieved an average accuracy rate of 89.36% by classifying the contents according to eight predefined areas of interest. Moreover, analyses were conducted based on the publication dates of all foreign news content, revealing relationships between the dates of publication and the classified areas of interest. Additionally, sentiment analysis was conducted on the collected foreign news content using the BERT algorithm, which identified the sentiment categories of the contents and examined the perception of Turkey in the foreign press.

Anahtar Kelimeler

sentiment analysis , BERT , text mining , data mining , news analysis , classification of news , relationship between news dates and content

Kaynakça

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