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Communities & Collections
Communities & Collections
Sentiment Analysis of Turkish Political News
Date
2012-12-07
Author
Kaya, Mesut
Fidan, Guven
Toroslu, İsmail Hakkı
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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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%.
Subject Keywords
NLP
,
News domain
,
Machine learning
,
Turkish
,
Sentiment analysis
URI
https://hdl.handle.net/11511/36203
DOI
https://doi.org/10.1109/wi-iat.2012.115
Collections
Department of Computer Engineering, Conference / Seminar