Aspect-Based Sentiment Analysis for Turkish
Reviews Using Token and Sequential Classification
Methods

Aspect-Based Sentiment Analysis for Turkish
Reviews Using Token and Sequential Classification
Methods

token classification; sequential model classification;
aspect term; aspect-based sentiment analysis; deep learning, turkish

Aspect-Based Sentiment Analysis (ABSA) aims to
identify sentiments expressed toward specific aspects or attributes
of entities in text. This study addresses the under-explored area of
ABSA in the Turkish language by extracting aspect terms (targets)
and their categories from customer reviews and determining the
sentiment polarity for each aspect. Turkish, being a
morphologically rich and structurally complex language, poses
unique challenges that often hinder the direct application of
methods developed for other languages. Hence, developing
sentiment analysis approaches tailored to Turkish is of significant
importance. We propose a two-stage pipeline: a token-level
classification to recognize aspect terms and assign them to one of
12 predefined aspect categories, followed by a sequence-level
(sentence-level) classification to predict sentiment (positive,
negative, or neutral) for each identified aspect. We fine-tuned five
transformer-based language models (BERT, ConvBERT,
ELECTRA, DeBERTa, and DistilBERT) for aspect term and
category extraction, and four models (BERT, ConvBERT,
ELECTRA, DistilBERT) for sentiment classification.
Experimental results on the SemEval-2016 Turkish ABSA
Restaurant dataset show that the BERT model achieved the
highest accuracy (92.20%) for aspect term and category
identification, closely followed by ConvBERT (91.68%). For
sentiment analysis, ConvBERT performed best with an accuracy
of 86.91%, outperforming ELECTRA (85.34%), BERT (82.75%),
and DistilBERT (77.48%). These findings demonstrate that pretrained transformer models can effectively handle fine-grained
sentiment analysis in Turkish, substantially improving on
previous approaches. The proposed pipeline and comparative
results provide a novel benchmark for Turkish ABSA, with
potential applications in analyzing Turkish customer feedback to
glean actionable insights.

Metin Bilgin

https://ijcit.com/index.php/ijcit/article/view/485

Department of Computer Engineering
Bursa Uludağ University of Turkey
Bursa,Turkey

september 2025

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