A Comparative Analysis of SVM, LSTM and CNN-RNN Models for the BBC News Classification

Karaman Y., AKDENİZ F., Savaş B. K., BECERİKLİ Y.

7th International Conference on Smart City Applications, SCA 2022, Castelo Branco, Portekiz, 19 - 21 Ekim 2022, cilt.629 LNNS, ss.473-483 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 629 LNNS
  • Doi Numarası: 10.1007/978-3-031-26852-6_44
  • Basıldığı Şehir: Castelo Branco
  • Basıldığı Ülke: Portekiz
  • Sayfa Sayıları: ss.473-483
  • Anahtar Kelimeler: BBC news classification, GloVe, Hybrid model, Natural language processing, Text document classification
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Developing technology, easier access to internet networks and the increase in the widespread use of the internet cause more text data to be produced than ever. Analyzing and classifying this data is one of the most difficult problems. Hence, automatic classification of text data is very important. In this study, the word embedding method, machine learning, deep learning and a hybrid model is proposed to classify news texts. GloVe method is used as a word embedding model and SVM (Support Vector Machine), LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network) + RNN (Recurrent Neural Networks) models are used to classify data. Also, various optimization algorithms have been tested in deep learning models and the results of these algorithms are in a comparative manner. BBC-News dataset is used to classify news texts. The best accuracy results have been obtained as 98.0% in the SVM model, 97.0% in the LSTM model and 96.0% in the CNN + RNN model.