Skip to main content
SpringerLink
Log in

Sentiment Analysis in Turkish Using Transformer-Based Deep Learning Models

  • Conference paper
  • First Online:
4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2022)

Part of the book series: Engineering Cyber-Physical Systems and Critical Infrastructures ((ECPSCI,volume 7))

Abstract

Social media, e-commerce, review, and blogging websites have become important sources of knowledge as information and communication technology has advanced. Individuals can share their thoughts, complaints, feelings, and views on a wide range of topics. Because it seeks to identify the orientation of the sentiment present in source materials, sentiment analysis is a key field of research in natural language processing. Sentiment analysis is a natural language processing (NLP) task that received the attention of many researchers and practitioners. The majority of earlier studies in sentiment analysis mainly focused on traditional machine learning (i.e., shallow learning) and, to some extent, deep learning algorithms. Recently, transformer-based models have been developed and applied in different application domains. These models have been shown to have a huge potential to advance text classification and, particularly, sentiment analysis research fields. In this paper, we investigate the performance of transformer-based sentiment analysis models. The case study has been performed on four datasets that are in Turkish. First, preprocessing methods were used to remove links, numerals, unmeaningful, and punctuation characters from the data. Unsuitable data was eliminated after the preprocessing phase. Second, each data set splitted into two parts; 80% for training, 20% for testing. Finally, transformer-based BERT, ConvBERT, ELECTRA, traditional deep learning, and machine learning algorithms have been applied to classify sentences into two or three classes, which are either positive, neutral, or negative. Experimental results demonstrated that transformer-based models could provide superior performance in terms of F-score compared to the traditional machine learning-based and deep learning models.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Internet live stats - internet usage; social media statistics. https://www.internetlivestats.com/. Accessed 6 Oct 2022

  2. Wikistats - statistics for wikimedia projects. https://stats.wikimedia.org/. Accessed 6 Oct 2022

  3. Abercrombie, G., Batista-Navarro, R.: Parlvote: a corpus for sentiment analysis of political debates. In: LREC (2020)

    Google Scholar 

  4. Al-Smadi, M., Talafha, B., Al-Ayyoub, M., Jararweh, Y.: Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int. J. Mach. Learn. Cybern. 10(8), 2163–2175 (2018). https://doi.org/10.1007/s13042-018-0799-4

    Article  Google Scholar 

  5. Alamoodi, A., et al.: Sentiment analysis and its applications in fighting covid-19 and infectious diseases: a systematic review. Expert Syst. Appl. 114155 (2020). https://doi.org/10.1016/j.eswa.2020.114155, http://www.sciencedirect.com/science/article/pii/S0957417420308988

  6. Antoun, W., Baly, F., Hajj, H.: Arabert: transformer-based model for Arabic language understanding. ArXiv abs/2003.00104 (2020)

    Google Scholar 

  7. Appel, O., Chiclana, F., Carter, J., Fujita, H.: A hybrid approach to the sentiment analysis problem at the sentence level. Knowl.-Based Syst. 108, 110–124 (2016). https://doi.org/10.1016/j.knosys.2016.05.040, http://www.sciencedirect.com/science/article/pii/S095070511630137X. New Avenues in Knowledge Bases for Natural Language Processing

  8. Arisoy, E., Sethy, A., Ramabhadran, B., Chen, S.: Bidirectional recurrent neural network language models for automatic speech recognition. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5421–5425 (2015). https://doi.org/10.1109/ICASSP.2015.7179007

  9. Ayata, D., Saraçlar, M., Özgür, A.: Turkish tweet sentiment analysis with word embedding and machine learning. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2017)

    Google Scholar 

  10. Bilen, B., Horasan, F.: Lstm network based sentiment analysis for customer reviews. Politeknik Dergisi, 1–1 (2021). https://doi.org/10.2339/politeknik.844019

  11. Bisong, E.: Google Colaboratory, pp. 59–64. Apress, Berkeley (2019). https://doi.org/10.1007/978-1-4842-4470-8_7

  12. Brown, T., et al.: Language models are few-shot learners. ArXiv abs/2005.14165 (2020)

    Google Scholar 

  13. Cetin, M., Amasyali, M.F.: Active learning for Turkish sentiment analysis. In: 2013 IEEE INISTA, pp. 1–4 (2013)

    Google Scholar 

  14. Chan, B., Schweter, S., Möller, T.: German’s next language model. ArXiv abs/2010.10906 (2020)

    Google Scholar 

  15. Ciftci, B., Apaydin, M.: A deep learning approach to sentiment analysis in Turkish. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), pp. 1–5 (2018)

    Google Scholar 

  16. Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: pre-training text encoders as discriminators rather than generators. ArXiv abs/2003.10555 (2020)

    Google Scholar 

  17. Dehkharghani, R., Saygin, Y., Yanikoglu, B.A., Oflazer, K.: Sentiturknet: a Turkish polarity lexicon for sentiment analysis. Lang. Resour. Eval. 50, 667–685 (2016)

    Article  Google Scholar 

  18. Demirci, G.M., Keskin, S., Doğan, G.: Sentiment analysis in Turkish with deep learning. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 2215–2221 (2019)

    Google Scholar 

  19. Demirtas, E., Pechenizkiy, M.: Cross-lingual polarity detection with machine translation. In: Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM’13. Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2502069.2502078

  20. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding (2019)

    Google Scholar 

  21. Dreisbach, C., Koleck, T.A., Bourne, P.E., Bakken, S.: A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int. J. Med. Inform. 125, 37–46 (2019). https://doi.org/10.1016/j.ijmedinf.2019.02.008, http://www.sciencedirect.com/science/article/pii/S1386505618313789

  22. Erşahin, B., Aktas, Ö., Kilinç, D., Ersahin, M.: A hybrid sentiment analysis method for Turkish. Turk. J. Electr. Eng. Comput. Sci. 27, 1780–1793 (2019)

    Article  Google Scholar 

  23. Çetin, F.S., Eryigit, G.: Türkçe hedef tabanlı duygu analizi İçin alt görevlerin İncelenmesi - hedef terim, hedef kategori ve duygu sınıfı belirleme (2018)

    Google Scholar 

  24. Çetin, F.S., Yildirim, E., Özbey, C., Eryigit, G.: Tgb at semeval-2016 task 5: Multi-lingual constraint system for aspect based sentiment analysis. In: SemEval@NAACL-HLT (2016)

    Google Scholar 

  25. Farahani, M., Gharachorloo, M., Farahani, M., Manthouri, M.: Parsbert: transformer-based model for Persian language understanding. ArXiv abs/2005.12515 (2020)

    Google Scholar 

  26. Greco, F., Polli, A.: Emotional text mining: customer profiling in brand management. Int. J. Inform. Manag. 51, 101934 (2020). https://doi.org/10.1016/j.ijinfomgt.2019.04.007, http://www.sciencedirect.com/science/article/pii/S0268401218313598

  27. Karamollaoğlu, H., Dogru, I., Dörterler, M., Utku, A., Yildiz, O.: Sentiment analysis on Turkish social media shares through lexicon based approach. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), pp. 45–49 (2018)

    Google Scholar 

  28. Kaya, M., Fidan, G., Toroslu, I.H.: Sentiment analysis of Turkish political news. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 174–180 (2012)

    Google Scholar 

  29. Kırelli, Y., Arslankaya, S.: Sentiment analysis of shared tweets on global warming on twitter with data mining methods: a case study on Turkish language. Comput. Intell. Neurosci. 2020 (2020)

    Google Scholar 

  30. Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach. ArXiv abs/1907.11692 (2019)

    Google Scholar 

  31. Maryame, N., Najima, D., Hasnae, R., Rachida, A.: State of the art of deep learning applications in sentiment analysis: psychological behavior prediction. In: Bhateja, V., Satapathy, S.C., Satori, H. (eds.) Embedded Systems and Artificial Intelligence, pp. 441–451. Springer Singapore, Singapore (2020)

    Chapter  Google Scholar 

  32. Mulki, H., Haddad, H., Ali, C.B., Babaoglu, I.: Preprocessing impact on turkish sentiment analysis. In: 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2018)

    Google Scholar 

  33. Onan, A.: Sentiment analysis in Turkish based on weighted word embeddings. In: 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2020)

    Google Scholar 

  34. Pejic-Bach, M., Bertoncel, T., Meško, M., Živko Krstić: Text mining of industry 4.0 job advertisements. Int. J. Inform. Manag. 50, 416–431 (2020). https://doi.org/10.1016/j.ijinfomgt.2019.07.014, http://www.sciencedirect.com/science/article/pii/S0268401218313677

  35. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: Semeval-2014 task 4: aspect based sentiment analysis. In: COLING 2014 (2014)

    Google Scholar 

  36. Rumelli, M., Akkuş, D., Kart, Ö., Isik, Z.: Sentiment analysis in turkish text with machine learning algorithms. In: 2019 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5 (2019)

    Google Scholar 

  37. Schweter, S.: Berturk - bert models for turkish (2020). https://doi.org/10.5281/zenodo.3770924

  38. Seyfioglu, M.S., Demirezen, M.: A hierarchical approach for sentiment analysis and categorization of turkish written customer relationship management data. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 361–365 (2017)

    Google Scholar 

  39. Sezgen, E., Mason, K.J., Mayer, R.: Voice of airline passenger: a text mining approach to understand customer satisfaction. J. Air Transp. Manag. 77, 65–74 (2019). https://doi.org/10.1016/j.jairtraman.2019.04.001, http://www.sciencedirect.com/science/article/pii/S0969699718304873

  40. Shehu, H., Sharif, M.H., Uyaver, S., Tokat, S., Ramadan, R.: Sentiment analysis of Turkish twitter data using polarity lexicon and artificial intelligence (2020)

    Google Scholar 

  41. Shehu, H.A., Tokat, S.: A hybrid approach for the sentiment analysis of Turkish twitter data. In: Hemanth, D.J., Kose, U. (eds.) Artificial Intelligence and Applied Mathematics in Engineering Problems, pp. 182–190. Springer International Publishing, Cham (2020)

    Chapter  Google Scholar 

  42. Sigirci, I.O., et al.: Sentiment analysis of Turkish reviews on google play store. In: 2020 5th International Conference on Computer Science and Engineering (UBMK), pp. 314–315 (2020)

    Google Scholar 

  43. Sutskever, I., Martens, J., Hinton, G.E.: Generating text with recurrent neural networks. In: L. Getoor, T. Scheffer (eds.) Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June - 2 July (2011), pp. 1017–1024 (2011). https://icml.cc/2011/papers/524_icmlpaper.pdf. Accessed 6 Oct 2022

  44. Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37, 267–307 (2011)

    Article  Google Scholar 

  45. Toh, Z., Su, J.: Nlangp at semeval-2016 task 5: Improving aspect based sentiment analysis using neural network features. In: SemEval@NAACL-HLT (2016)

    Google Scholar 

  46. Uslu, A., Tekin, S., Aytekin, T.: Sentiment analysis in Turkish film comments. In: 2019 27th Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2019)

    Google Scholar 

  47. Vaswani, A., et al.: Attention is all you need (2017)

    Google Scholar 

  48. de Vries, W., van Cranenburgh, A., Bisazza, A., Caselli, T., van Noord, G., Nissim, M.: Bertje: a dutch bert model. ArXiv abs/1912.09582 (2019)

    Google Scholar 

  49. Vural, A., Cambazoglu, B.B., Senkul, P., Tokgoz, Z.O.: A framework for sentiment analysis in Turkish: application to polarity detection of movie reviews in Turkish. In: ISCIS (2012)

    Google Scholar 

  50. Wolf, T., et al.: Transformers: State-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38–45. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-demos.6

  51. Xing, F.Z., Malandri, L., Zhang, Y., Cambria, E.: Financial sentiment analysis: an investigation into common mistakes and silver bullets. In: COLING (2020)

    Google Scholar 

  52. Yan, S.: Understanding LSTM and its diagrams (2017). https://blog.mlreview.com/understanding-lstm-and-its-diagrams-37e2f46f1714. Accessed 6 Oct 2022

  53. Yang, P., Chen, Y.: A survey on sentiment analysis by using machine learning methods. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 117–121 (2017)

    Google Scholar 

  54. Yildirim, S.: Comparing deep neural networks to traditional models for sentiment analysis in Turkish language (2020)

    Google Scholar 

  55. Yılmaz, B.: Product comments dataset (2020). https://www.kaggle.com/baharyilmaz/product-comments-dataset Accessed 6 Oct 2022

  56. Yurtalan, G., Koyuncu, M.: Çigdem Turhan: a polarity calculation approach for lexicon-based Turkish sentiment analysis. Turk. J. Electr. Eng. Comput. Sci. 27, 1325–1339 (2019)

    Article  Google Scholar 

  57. Zhu, Y., et al.: Aligning books and movies: towards story-like visual explanations by watching movies and reading books (2015)

    Google Scholar 

Download references

Acknowledgements

We thank Stefan Schweter for providing fine-tuned Turkish BERT model for the community.

Author information

Authors and Affiliations

  1. School of Computing, Wichita State University, Wichita, KS, 67260, USA

    Oktay Ozturk

  2. Department of Computer Engineering, Akdeniz University, Antalya, Turkey

    Alper Ozcan

Authors
  1. Oktay Ozturk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alper Ozcan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oktay Ozturk .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Karunya Nagar, Tamil Nadu, India

    D. Jude Hemanth

  2. Department of Computer Engineering, Faculty of Engineering, Süleyman Demirel University, Isparta, Türkiye

    Tuncay Yigit

  3. Department of Computer Engineering, Suleyman Demirel University, Isparta, Türkiye

    Utku Kose

  4. Electric-Electronic Department, Duzce University, Düzce, Türkiye

    Ugur Guvenc

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ozturk, O., Ozcan, A. (2023). Sentiment Analysis in Turkish Using Transformer-Based Deep Learning Models. In: Hemanth, D.J., Yigit, T., Kose, U., Guvenc, U. (eds) 4th International Conference on Artificial Intelligence and Applied Mathematics in Engineering. ICAIAME 2022. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-031-31956-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation