Sentiment Analysis of Turkish Political News


Kaya M., Fidan G., TOROSLU İ. H.

11th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Macau, Çin, 4 - 07 Aralık 2012, ss.174-180 identifier identifier

  • Doi Numarası: 10.1109/wi-iat.2012.115
  • Basıldığı Şehir: Macau
  • Basıldığı Ülke: Çin
  • Sayfa Sayıları: ss.174-180

Özet

In this paper, sentiment classification techniques are incorporated into the domain of political news from columns in different Turkish news sites. We compared four supervised machine learning algorithms of Naive Bayes, Maximum Entropy, SVM and the character based N-Gram Language Model for sentiment classification of Turkish political columns. We also discussed in detail the problem of sentiment classification in the political news domain. We observe from empirical findings that the Maximum Entropy and N-Gram Language Model outperformed the SVM and Naive Bayes. Using different features, all the approaches reached accuracies of 65% to 77%.