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Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

Published online by Cambridge University Press:  17 April 2020

Cem Rıfkı Aydın  and
Tunga Güngör
Cem Rıfkı Aydın*
Affiliation:
Department of Computer Engineering, Boğaziçi University, Istanbul34342, Turkey
Tunga Güngör
Affiliation:
Department of Computer Engineering, Boğaziçi University, Istanbul34342, Turkey
*
*Corresponding author. E-mail: cem.aydin1@boun.edu.tr
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Abstract

Although many studies on sentiment analysis have been carried out for widely spoken languages, this topic is still immature for Turkish. Most of the works in this language focus on supervised models, which necessitate comprehensive annotated corpora. There are a few unsupervised methods, and they utilize sentiment lexicons either built by translating from English lexicons or created based on corpora. This results in improper word polarities as the language and domain characteristics are ignored. In this paper, we develop unsupervised (domain-independent) and semi-supervised (domain-specific) methods for Turkish, which are based on a set of antonym word pairs as seeds. We make a comprehensive analysis of supervised methods under several feature weighting schemes. We then form ensemble of supervised classifiers and also combine the unsupervised and supervised methods. Since Turkish is an agglutinative language, we perform morphological analysis and use different word forms. The methods developed were tested on two datasets having different styles in Turkish and also on datasets in English to show the portability of the approaches across languages. We observed that the combination of the unsupervised and supervised approaches outperforms the other methods, and we obtained a significant improvement over the state-of-the-art results for both Turkish and English.

Keywords

Sentiment analysisOpinion miningMachine learningText classificationMorphological analysis
Type
Article
Information
Natural Language Engineering , Volume 27 , Issue 4 , July 2021 , pp. 455 - 483
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Copyright
© Cambridge University Press 2020

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