Research Article

Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts

Volume: 14 October 13, 2026
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Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts

Abstract

The rapid rise of e-commerce platforms has transformed user reviews into a significant data source. While these reviews present an important opportunity to understand user sentiments, conducting sentiment analysis in the Turkish language poses various challenges. The complex linguistic structure of Turkish and the limited number of studies in the literature restrict the accuracy and scope of analyses in this domain. Particularly, punctuation marks, grammatical structures, and other linguistic features are key aspects to consider during the sentiment analysis process. In this study, the TRSAv1 dataset was utilized to examine the impact of linguistic features specific to the Turkish language on sentiment classification. Morphological, syntactic, and semantic features were extracted from the texts, and their contributions to classification performance were evaluated. The study analyzed the influence of linguistic differences across various sentiment classes. A classification accuracy of 89% was achieved for positive-negative sentiment analysis, whereas distinguishing between positive and neutral sentiments was found to be more challenging, resulting in a lower performance. This study aims to contribute to the literature by revealing the effects of Turkish-specific linguistic features on sentiment analysis, thereby addressing a critical gap in this area of research. 

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Publication Date

October 13, 2026

Submission Date

July 11, 2025

Acceptance Date

August 11, 2025

Published in Issue

Year 2026 Volume: 14

APA
Çetintaş, D., & Yücel, Z. (2026). Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts. Balkan Journal of Electrical and Computer Engineering, 14, 260-266. https://doi.org/10.17694/bajece.1740158
AMA
1.Çetintaş D, Yücel Z. Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts. Balkan Journal of Electrical and Computer Engineering. 2026;14:260-266. doi:10.17694/bajece.1740158
Chicago
Çetintaş, Dilber, and Zehra Yücel. 2026. “Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts”. Balkan Journal of Electrical and Computer Engineering 14 (October): 260-66. https://doi.org/10.17694/bajece.1740158.
EndNote
Çetintaş D, Yücel Z (October 1, 2026) Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts. Balkan Journal of Electrical and Computer Engineering 14 260–266.
IEEE
[1]D. Çetintaş and Z. Yücel, “Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts”, Balkan Journal of Electrical and Computer Engineering, vol. 14, pp. 260–266, Oct. 2026, doi: 10.17694/bajece.1740158.
ISNAD
Çetintaş, Dilber - Yücel, Zehra. “Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts”. Balkan Journal of Electrical and Computer Engineering 14 (October 1, 2026): 260-266. https://doi.org/10.17694/bajece.1740158.
JAMA
1.Çetintaş D, Yücel Z. Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts. Balkan Journal of Electrical and Computer Engineering. 2026;14:260–266.
MLA
Çetintaş, Dilber, and Zehra Yücel. “Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts”. Balkan Journal of Electrical and Computer Engineering, vol. 14, Oct. 2026, pp. 260-6, doi:10.17694/bajece.1740158.
Vancouver
1.Dilber Çetintaş, Zehra Yücel. Classification by Feature Fusion: The Impact of Linguistic Features on Sentiment Analysis in Turkish Texts. Balkan Journal of Electrical and Computer Engineering. 2026 Oct. 1;14:260-6. doi:10.17694/bajece.1740158

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