Research Article
Year 2025,
Volume: 8 Issue: 4, 677 - 687
Abstract
References
- M. S. Akin, “Enhancing e-commerce competitiveness: A comprehensive analysis of customer experiences and strategies in the Turkish market,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 1, p. 100222, Mar. 2024, doi: 10.1016/j.joitmc.2024.100222.
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- M. T. Barutcu and B. Basak, “Customer Complaints about E-Commerce Sites: Content Analysis,” 2018.
- Elif Ayanoğlu, Zeynep Çolak, Toygar Tanyel, Hasan Yunus Sarıoğlu, and Banu Diri, “Detection and Classification of Customer Comments Containing Complaints,” Nov. 2023, doi: 10.5281/ZENODO.10254498.
- M. Demircan, A. Seller, F. Abut, and M. F. Akay, “Developing Turkish sentiment analysis models using machine learning and e-commerce data,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 202–207, Jun. 2021, doi: 10.1016/j.ijcce.2021.11.003.
- J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, p. 100059, Mar. 2024, doi: 10.1016/j.nlp.2024.100059.
- A. Ocal, “Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach,” IEEE Access, vol. 12, pp. 182386–182409, 2024, doi: 10.1109/ACCESS.2024.3510526.
- A. Ocal, “Framing, Emotions, Salience: The Future of AI as Seen by Redditors,” Ph.D., Syracuse University, United States -- New York, 2023. Accessed: Oct. 19, 2023. [Online]. Available: https://www.proquest.com/docview/2845416849
- A. Ocal and K. Crowston, “Framing and feelings on social media: the futures of work and intelligent machines,” ITP, vol. 37, no. 7, pp. 2462–2488, Apr. 2024, doi: 10.1108/ITP-01-2023-0049.
- A. Öcal, L. Xiao, and J. Park, “Reasoning in social media: insights from Reddit ‘Change My View’ submissions,” Online Information Review, vol. 45, no. 7, pp. 1208–1226, Jan. 2021, doi: 10.1108/OIR-08-2020-0330.
- A. Ocal, “Perceptions of AI Ethics on Social Media,” in 2023 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS), IEEE, 2023. doi: 10.1109/ETHICS57328.2023.10155069.
- S. F. Yilmaz, E. B. Kaynak, A. Koc, H. Dibeklioglu, and S. S. Kozat, “Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance,” IEEE Trans. Neural Netw. Learning Syst., vol. 34, no. 1, pp. 331–343, Jan. 2023, doi: 10.1109/TNNLS.2021.3094304.
- İ. Yurtseven, S. Bagriyanik, and S. Ayvaz, “A Review of Spam Detection in Social Media,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), Sep. 2021, pp. 383–388. doi: 10.1109/UBMK52708.2021.9558993.
- S. Ayvaz, S. Yıldırım, and Y. B. Salman, “Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması,” European Journal of Science and Technology, pp. 51–60, Aug. 2019, doi: 10.31590/ejosat.537085.
- N. Öztürk and S. Ayvaz, “Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis,” Telematics and Informatics, vol. 35, no. 1, pp. 136–147, Apr. 2018, doi: 10.1016/j.tele.2017.10.006.
- S. M. Mohammad and P. D. Turney, “Crowdsourcing A Word–Emotion Association Lexicon,” Computational Intelligence, vol. 29, no. 3, pp. 436–465, Aug. 2013, doi: 10.1111/j.1467-8640.2012.00460.x.
- W. van Atteveldt, M. A. C. G. van der Velden, and M. Boukes, “The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms,” Communication Methods and Measures, vol. 15, no. 2, pp. 121–140, Apr. 2021, doi: 10.1080/19312458.2020.1869198.
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186.
- U. U. Acikalin, B. Bardak, and M. Kutlu, “Turkish Sentiment Analysis Using BERT,” in 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey: IEEE, Oct. 2020. doi: 10.1109/siu49456.2020.9302492.
- B. Ciftci and M. S. Apaydin, “A Deep Learning Approach to Sentiment Analysis in Turkish,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey: IEEE, Sep. 2018, pp. 1–5. doi: 10.1109/idap.2018.8620751.
- G. Yavuz, “Web Kazima Ve Duygu Analizi Temelli Ürün Analiz Sistemi,” 2023. Master's Thesis.
- M. Masarifoglu et al., “Sentiment Analysis of Customer Comments in Banking using BERT-based Approaches,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey: IEEE, Jun. 2021, pp. 1–4. doi: 10.1109/siu53274.2021.9477890.
- M. Arzu and M. Aydoğan, “Türkçe Duygu Sınıflandırma İçin Transformers Tabanlı Mimarilerin Karşılaştırılmalı Analizi,” JCS, Aug. 2023, doi: 10.53070/bbd.1350405.
- N. Paker and B. Kizilirmak, “Çevrim İçi Müşteri Yorumlarını Etkileyen Faktörler Üzerine Keşifsel Bir Çalışma: Trendyol Örneği,” Anadolu Üniversitesi Sosyal Bilimler Dergisi, vol. 23, no. 4, pp. 1393–1414, Dec. 2023, doi: 10.18037/ausbd.1309934.
- S. İLhan Omurca, E. Eki̇Nci̇, E. Yakupoğlu, E. Arslan, and B. Çapar, “Automatic Detection of the Topics in Customer Complaints with Artificial Intelligence,” Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 3, pp. 268–277, Jul. 2021, doi: 10.17694/bajece.832274.
- B. Teke, S. N. Yazıcı, G. Zamir, A. B. Budak, and I. Karabey Aksakallı, “BERTurk-Based Sentiment Analysis on E-Commerce Multi Domain Product Reviews,” Afyon Kocatepe University Journal of Sciences and Engineering, vol. 25, no. 3, pp. 497–509, May 2025, doi: 10.35414/akufemubid.1537513.
- S. Yildirim, “Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks,” Jan. 30, 2024, arXiv: arXiv:2401.17396. doi: 10.48550/arXiv.2401.17396.
- P. Savci and B. Das, “Prediction of the customers’ interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 227–237, Mar. 2023, doi: 10.1016/j.jksuci.2023.02.017.
- S. Sinha, A. Jayan, and R. Kumar, “An Analysis and Comparison of Deep-Learning Techniques and Hybrid Model for Sentiment Analysis for Movie Review,” in 2022 3rd International Conference for Emerging Technology (INCET), May 2022, pp. 1–5. doi: 10.1109/INCET54531.2022.9824630.
- T. Tanyel, B. Alkurdi, and S. Ayvaz, Linguistic-based Data Augmentation Approach for Offensive Language Detection. 2022, p. 6. doi: 10.1109/UBMK55850.2022.9919562.
- M. Gürbüz and M. Kotan, “Multi-Category E-Commerce Insights via Social Media Analysis using Machine Learning and BERT,” acin, vol. 0, no. 0, pp. 0–0, Feb. 2025, doi: 10.26650/acin.1483488.
- A. Dalgali and K. Crowston, “Sharing Open Deep Learning Models,” presented at the Hawai’i International Conference on System Science., Jan. 2019. doi: 10.24251/HICSS.2019.256.
- A. Dalgali and K. Crowston, “Algorithmic Journalism and Its Impacts on Work,” Computation + Journalism Symposium, 2020, [Online]. Available: https://cj2020.northeastern.edu/
- A. Dalgali and K. Crowston, “Factors Influencing Approval of Wikipedia Bots,” in The Hawaii International Conference on System Sciences, 2020, p. 10. [Online]. Available: http://hdl.handle.net/10125/63757
- M. Bozuyla, “Sentiment Analysis of Turkish Drug Reviews with Bidirectional Encoder Representations from Transformers,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 23, no. 1, pp. 1–17, Jan. 2024, doi: 10.1145/3626523.
- H. A. Love et al., “The Future of Work in the Age of Automation: Proceedings of a Workshop on Norbert Wiener’s 21st Century Legacy,” IEEE Trans. Technol. Soc., pp. 1–23, 2024, doi: 10.1109/TTS.2024.3476041.
Year 2025,
Volume: 8 Issue: 4, 677 - 687
Abstract
This study examines sentiment analysis in Turkish e-commerce product reviews by comparing two distinct approaches: classification based on star ratings and textual sentiment using a BERT-based model. Two models were fine-tuned for this purpose: Model 1, trained on numerical star ratings, and Model 2, trained on manually labeled sentiment in review texts, to evaluate their performance in accurately capturing customer sentiment. The results reveal that star ratings often fail to reflect true sentiment, as many users assign high ratings despite expressing negative opinions in the text. Model 1 tended to overclassify reviews as negative, while Model 2, which used direct text sentiment labels, provided a more balanced classification across sentiment categories. Chi-square tests confirmed a statistically significant difference between the predictions of the two models, highlighting the impact of labeling methods on model behavior. Furthermore, our findings reinforce the value of deep learning approaches, particularly transformer-based models like BERT, in processing Turkish-language texts, which pose challenges for traditional dictionary-based methods due to their complex morphology and syntax. From a business perspective, relying solely on star ratings may lead to an inaccurate interpretation of sentiment. Incorporating text-based analysis can offer more precise insights into customer satisfaction. Future research may explore multimodal sentiment analysis by integrating visual or video data and examining how AI-driven sentiment systems influence decision-making processes across different sectors.
References
- M. S. Akin, “Enhancing e-commerce competitiveness: A comprehensive analysis of customer experiences and strategies in the Turkish market,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 1, p. 100222, Mar. 2024, doi: 10.1016/j.joitmc.2024.100222.
- N. Yücel and Ö. Cömert, “Müşteri Duyarlılığını Keşfetmek İçin Yapay Zeka Destekli Analiz ile Çevrimiçi Ürün İncelemelerinden Anlamlı Bilgiler Elde Etme,” Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 35, no. 2, pp. 679–690, Sep. 2023, doi: 10.35234/fumbd.1305932.
- M. T. Barutcu and B. Basak, “Customer Complaints about E-Commerce Sites: Content Analysis,” 2018.
- Elif Ayanoğlu, Zeynep Çolak, Toygar Tanyel, Hasan Yunus Sarıoğlu, and Banu Diri, “Detection and Classification of Customer Comments Containing Complaints,” Nov. 2023, doi: 10.5281/ZENODO.10254498.
- M. Demircan, A. Seller, F. Abut, and M. F. Akay, “Developing Turkish sentiment analysis models using machine learning and e-commerce data,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 202–207, Jun. 2021, doi: 10.1016/j.ijcce.2021.11.003.
- J. R. Jim, M. A. R. Talukder, P. Malakar, M. M. Kabir, K. Nur, and M. F. Mridha, “Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review,” Natural Language Processing Journal, vol. 6, p. 100059, Mar. 2024, doi: 10.1016/j.nlp.2024.100059.
- A. Ocal, “Perceptions of the Future of Artificial Intelligence on Social Media: A Topic Modeling and Sentiment Analysis Approach,” IEEE Access, vol. 12, pp. 182386–182409, 2024, doi: 10.1109/ACCESS.2024.3510526.
- A. Ocal, “Framing, Emotions, Salience: The Future of AI as Seen by Redditors,” Ph.D., Syracuse University, United States -- New York, 2023. Accessed: Oct. 19, 2023. [Online]. Available: https://www.proquest.com/docview/2845416849
- A. Ocal and K. Crowston, “Framing and feelings on social media: the futures of work and intelligent machines,” ITP, vol. 37, no. 7, pp. 2462–2488, Apr. 2024, doi: 10.1108/ITP-01-2023-0049.
- A. Öcal, L. Xiao, and J. Park, “Reasoning in social media: insights from Reddit ‘Change My View’ submissions,” Online Information Review, vol. 45, no. 7, pp. 1208–1226, Jan. 2021, doi: 10.1108/OIR-08-2020-0330.
- A. Ocal, “Perceptions of AI Ethics on Social Media,” in 2023 IEEE International Symposium on Ethics in Engineering, Science and Technology (ETHICS), IEEE, 2023. doi: 10.1109/ETHICS57328.2023.10155069.
- S. F. Yilmaz, E. B. Kaynak, A. Koc, H. Dibeklioglu, and S. S. Kozat, “Multi-Label Sentiment Analysis on 100 Languages With Dynamic Weighting for Label Imbalance,” IEEE Trans. Neural Netw. Learning Syst., vol. 34, no. 1, pp. 331–343, Jan. 2023, doi: 10.1109/TNNLS.2021.3094304.
- İ. Yurtseven, S. Bagriyanik, and S. Ayvaz, “A Review of Spam Detection in Social Media,” in 2021 6th International Conference on Computer Science and Engineering (UBMK), Sep. 2021, pp. 383–388. doi: 10.1109/UBMK52708.2021.9558993.
- S. Ayvaz, S. Yıldırım, and Y. B. Salman, “Türkçe Duygu Kütüphanesi Geliştirme: Sosyal Medya Verileriyle Duygu Analizi Çalışması,” European Journal of Science and Technology, pp. 51–60, Aug. 2019, doi: 10.31590/ejosat.537085.
- N. Öztürk and S. Ayvaz, “Sentiment analysis on Twitter: A text mining approach to the Syrian refugee crisis,” Telematics and Informatics, vol. 35, no. 1, pp. 136–147, Apr. 2018, doi: 10.1016/j.tele.2017.10.006.
- S. M. Mohammad and P. D. Turney, “Crowdsourcing A Word–Emotion Association Lexicon,” Computational Intelligence, vol. 29, no. 3, pp. 436–465, Aug. 2013, doi: 10.1111/j.1467-8640.2012.00460.x.
- W. van Atteveldt, M. A. C. G. van der Velden, and M. Boukes, “The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms,” Communication Methods and Measures, vol. 15, no. 2, pp. 121–140, Apr. 2021, doi: 10.1080/19312458.2020.1869198.
- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of NAACL-HLT, Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186.
- U. U. Acikalin, B. Bardak, and M. Kutlu, “Turkish Sentiment Analysis Using BERT,” in 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey: IEEE, Oct. 2020. doi: 10.1109/siu49456.2020.9302492.
- B. Ciftci and M. S. Apaydin, “A Deep Learning Approach to Sentiment Analysis in Turkish,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey: IEEE, Sep. 2018, pp. 1–5. doi: 10.1109/idap.2018.8620751.
- G. Yavuz, “Web Kazima Ve Duygu Analizi Temelli Ürün Analiz Sistemi,” 2023. Master's Thesis.
- M. Masarifoglu et al., “Sentiment Analysis of Customer Comments in Banking using BERT-based Approaches,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey: IEEE, Jun. 2021, pp. 1–4. doi: 10.1109/siu53274.2021.9477890.
- M. Arzu and M. Aydoğan, “Türkçe Duygu Sınıflandırma İçin Transformers Tabanlı Mimarilerin Karşılaştırılmalı Analizi,” JCS, Aug. 2023, doi: 10.53070/bbd.1350405.
- N. Paker and B. Kizilirmak, “Çevrim İçi Müşteri Yorumlarını Etkileyen Faktörler Üzerine Keşifsel Bir Çalışma: Trendyol Örneği,” Anadolu Üniversitesi Sosyal Bilimler Dergisi, vol. 23, no. 4, pp. 1393–1414, Dec. 2023, doi: 10.18037/ausbd.1309934.
- S. İLhan Omurca, E. Eki̇Nci̇, E. Yakupoğlu, E. Arslan, and B. Çapar, “Automatic Detection of the Topics in Customer Complaints with Artificial Intelligence,” Balkan Journal of Electrical and Computer Engineering, vol. 9, no. 3, pp. 268–277, Jul. 2021, doi: 10.17694/bajece.832274.
- B. Teke, S. N. Yazıcı, G. Zamir, A. B. Budak, and I. Karabey Aksakallı, “BERTurk-Based Sentiment Analysis on E-Commerce Multi Domain Product Reviews,” Afyon Kocatepe University Journal of Sciences and Engineering, vol. 25, no. 3, pp. 497–509, May 2025, doi: 10.35414/akufemubid.1537513.
- S. Yildirim, “Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks,” Jan. 30, 2024, arXiv: arXiv:2401.17396. doi: 10.48550/arXiv.2401.17396.
- P. Savci and B. Das, “Prediction of the customers’ interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 3, pp. 227–237, Mar. 2023, doi: 10.1016/j.jksuci.2023.02.017.
- S. Sinha, A. Jayan, and R. Kumar, “An Analysis and Comparison of Deep-Learning Techniques and Hybrid Model for Sentiment Analysis for Movie Review,” in 2022 3rd International Conference for Emerging Technology (INCET), May 2022, pp. 1–5. doi: 10.1109/INCET54531.2022.9824630.
- T. Tanyel, B. Alkurdi, and S. Ayvaz, Linguistic-based Data Augmentation Approach for Offensive Language Detection. 2022, p. 6. doi: 10.1109/UBMK55850.2022.9919562.
- M. Gürbüz and M. Kotan, “Multi-Category E-Commerce Insights via Social Media Analysis using Machine Learning and BERT,” acin, vol. 0, no. 0, pp. 0–0, Feb. 2025, doi: 10.26650/acin.1483488.
- A. Dalgali and K. Crowston, “Sharing Open Deep Learning Models,” presented at the Hawai’i International Conference on System Science., Jan. 2019. doi: 10.24251/HICSS.2019.256.
- A. Dalgali and K. Crowston, “Algorithmic Journalism and Its Impacts on Work,” Computation + Journalism Symposium, 2020, [Online]. Available: https://cj2020.northeastern.edu/
- A. Dalgali and K. Crowston, “Factors Influencing Approval of Wikipedia Bots,” in The Hawaii International Conference on System Sciences, 2020, p. 10. [Online]. Available: http://hdl.handle.net/10125/63757
- M. Bozuyla, “Sentiment Analysis of Turkish Drug Reviews with Bidirectional Encoder Representations from Transformers,” ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol. 23, no. 1, pp. 1–17, Jan. 2024, doi: 10.1145/3626523.
- H. A. Love et al., “The Future of Work in the Age of Automation: Proceedings of a Workshop on Norbert Wiener’s 21st Century Legacy,” IEEE Trans. Technol. Soc., pp. 1–23, 2024, doi: 10.1109/TTS.2024.3476041.
There are 36 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | October 13, 2025 |
| Publication Date | October 16, 2025 |
| Submission Date | July 21, 2025 |
| Acceptance Date | August 25, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 4 |
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