Turkish Text Classification Based On Wrapper Feature Selection Using Particle Swarm Optimization

Year 2024, Volume: 24 Issue: 05, 1180 - 1188

Ezgi Zorarpacı

Abstract

The vast majority of the digital era data is stored as text. Text mining is an integral part of data mining. Text classification (TC) is a natural language processing (NLP) operation often needed in text mining. This operation is needed in numerous kinds of research such as information retrieval, document classification, language detection, sentiment analysis, etc. According to the literature, the filter feature selection methods have often been applied to reduce the dimensionality of data in Turkish TC. However, the wrapper-based feature selection methods can provide better classification accuracies than the filter methods. Motivated by this idea, a Turkish TC method based on wrapper feature selection using particle swarm optimization algorithm (PSO) and multinomial naive bayes (MNB) classifier is proposed in this study. TTC-3600 Turkish news texts are used for TC in the experiments. The proposed method achieves a classification accuracy of 94.55% on TTC-3600 Turkish news text dataset by using stemming Tf-Idf features. Hence, it produces competitive accuracies to the cutting-edge Turkish TC methods.

Keywords

Feature selection, Natural language processing, text classification, text mining

Parçacık Sürü Optimizasyonunu Kullanan Sarmalayıcı Öznitelik Seçimine Dayalı Türkçe Metin Sınıflandırma

Abstract

Dijital çağ verilerinin büyük çoğunluğu metin olarak depolanmaktadır. Metin madenciliği veri madenciliğinin ayrılmaz bir parçasıdır. Metin sınıflandırma (TC), metin madenciliğinde sıklıkla ihtiyaç duyulan bir doğal dil işleme (NLP) işlemidir. Bu işleme bilgi erişimi, belge sınıflandırma, dil tespiti, duygu analizi vb. birçok araştırmada ihtiyaç duyulmaktadır. Literatüre göre, Türkçe TC'de veri boyutunun azaltması için filtre öznitelik seçme yöntemleri sıklıkla uygulanmaktadır. Ancak sarmalayıcı tabanlı öznitelik seçme yöntemleri, filtre yöntemlerine kıyasla daha iyi sınıflandırma doğruluğu sağlayabilir. Bu fikirden hareketle, bu çalışmada parçacık sürüsü optimizasyon algoritması (PSO) ve çok terimli naive bayes (MNB) sınıflandırıcısını kullanan sarmalayıcı öznitelik seçim yöntemi tabanlı bir Türkçe TC metodu önerilmektedir. Deneylerde TC için TTC-3600 Türkçe haber metinleri kullanılmıştır. Önerilen yöntem, köklerine ayrılmış (stemming) Tf-Idf özniteliklerini kullanarak TTC-3600 Türkçe haber metni veri kümesinde %94,55'lik bir sınıflandırma doğruluğuna ulaşmaktadır. Böylece en son Türkçe TC yöntemleriyle rekabet edebilen sınıflandırma doğrulukları üretmektedir.

Keywords

Öznitelik seçimi, Doğal dil işleme, Metin sınıflandırma, Metin madenciliği

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