Sentiment Analysis of Turkish Political News

Abstract

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 Naïve 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 Naïve Bayes. Using different features, all the approaches reached accuracies of 65% to 77%.

Sentiment Analysis in Turkish

“…Kaya et al (2012) investigated the performances of supervised machine learning algorithms of Naive Bayes, maximum entropy, SVM and the character based n-gram language models. It was observed that maximum entropy and the n-gram outperformed the SVM and Naive Bayes and achieved 76-77% accuracy with different features.…”

“…It was observed that maximum entropy and the n-gram outperformed the SVM and Naive Bayes and achieved 76-77% accuracy with different features. Subsequently, Kaya (2013) described an improved version of their previous system by implementing transfer learning into the existing framework. This system accomplished more than a 25% improvement over the previous system and with all of the three machine learning approaches (Naive Bayes, SVM and maximum entropy), accuracy values over 90% were obtained for the sentiment classification of Turkish political columns.…”

The impact of term weighting method on Twitter sentiment analysis

“…gibi mesaj içeriğinde gözlenen nitelikler kullanılmaktadır [21], [25], [26]. Ayrıca, ön tanımlı sözlükler ve semantik işlemlere ihtiyaç duyulan doğrusal sınıflandırıcılar etiketleme amacıyla kullanılmaktadır [27], [28]. Bu yöntemlerle duygu analizinde elde edilen başarı oranının ise %59-87 arasında olduğu görülmektedir [30].…”

Sentiment analysis for Turkish Twitter feeds

“…Literatürde duygu analizi alanındaİngilizce için birçok istatistiksel ve dilbilimsel çalışma yapılmış olmakla beraber [6][7][8][9] Türkçe için henüz çok fazla çalışma yayınlanmamıştır [5], [14]. Bu bildiride öncelikli olarak Twitter üzerinden çekilen Türkçe bir veri seti oluşturulmuştur.…”