Sentiment Analysis of Turkish Political News

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%.

[1]  Bob Carpenter,et al.  Scaling High-Order Character Language Models to Gigabytes , 2005, ACL 2005.

[2]  John D. Lafferty,et al.  Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  B. Alexandra,et al.  Rethinking Sentiment Analysis in the News: from Theory to Practice and back , 2009 .

[4]  Alison Huettner,et al.  Fuzzy Typing for Document Management , 2000 .

[5]  Bruno Pouliquen,et al.  Sentiment Analysis in the News , 2010, LREC.

[6]  Hong Yu,et al.  Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences , 2003, EMNLP.

[7]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[8]  Marie-Francine Moens,et al.  A machine learning approach to sentiment analysis in multilingual Web texts , 2009, Information Retrieval.

[9]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[10]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[11]  Marti A. Hearst.  Direction-based text interpretation as an information access refinement , 1992 .

[12]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[13]  Warren Sack,et al.  On the Computation of Point of View , 1994, AAAI.

[14]  Michael L. Littman,et al.  Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus , 2002, ArXiv.

[15]  Steven Skiena,et al.  Large-Scale Sentiment Analysis for News and Blogs (system demonstration) , 2007, ICWSM.

[16]  Nello Cristianini,et al.  Detection of Bias in Media Outlets with Statistical Learning Methods , 2009 .

[17]  Carlo Strapparava,et al.  SemEval-2007 Task 14: Affective Text , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[18]  Rob Malouf,et al.  A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[19]  Vasileios Hatzivassiloglou,et al.  Predicting the Semantic Orientation of Adjectives , 1997, ACL.

[20]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.

[21]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.