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COVID-19 Hakkındaki Türkçe Tweetlerde LSTM Ağı Kullanılarak Duygu Sınıflandırması

Year 2023, Volume: 11 Issue: 2, 341 - 353, 01.06.2023
https://doi.org/10.36306/konjes.1173939

Abstract

Covid-19 pandemisi herkesi çeşitli yönlerden etkilediğinden, pandemi nedeniyle insanlar daha çok sosyal medya platformlarında bu yönlere ilişkin görüşlerini dile getiriyorlar. Bu görüşler, pandemiye yönelik duyguları anlamada çok önemli bir rol oynamaktadır. Bu çalışmada, 2020'den 2021'e kadar Covid-19 konulu Türkçe tweet'ler toplanmış ve önceden eğitilmiş bir metin sınıflandırıcı modeli kullanılarak duygu açısından olumlu, olumsuz veya nötr olarak etiketlenmiştir. Bu etiketli veri kümesini kullanarak, ikili ve çok sınıflı sınıflandırma görevleri için SVM, Naive Bayes, K-Nearest Neighbors ve CNN-LSTM model makine öğrenme algoritmaları için bir dizi deney gerçekleştirilmiştir.

References

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SENTIMENT CLASSIFICATION ON TURKISH TWEETS ABOUT COVID-19 USING LSTM NETWORK

Year 2023, Volume: 11 Issue: 2, 341 - 353, 01.06.2023
https://doi.org/10.36306/konjes.1173939

Abstract

As Covid-19 pandemic affected everyone in various aspects, people have been expressing their opinions on these aspects mostly on social media platforms because of the pandemic. These opinions play a crucial role in understanding the sentiments towards the pandemic. In this study, Turkish tweets on Covid-19 topic were collected from March 2020 to January 2021 and labelled as positive, negative, or neutral in terms of sentiment using BERT which is a pre-trained text classifier model. Using this labelled dataset, a set of experiments were carried out with SVM, Naive Bayes, K-Nearest Neighbors, and CNN-LSTM model machine learning algorithms for binary and multi-class classification tasks. Results of these experiments have shown that CNN-LSTM model outperforms other machine learning algorithms which are used in this study in both binary classification and multi-class classification tasks.

References

  • [1] H. Abid, J. Mohd, and V. Raju, "Effects of COVID 19 pandemic in daily life," Current Medicine Research and Practice, vol. 10, no. 2, pp. 78-79, 2020.
  • [2] B. Gokulakrishnan, P. Priyanthan, T. Ragavan, N. Prasath, and A. Perera, "Opinion mining and sentiment analysis on a twitter data stream," in International conference on advances in ICT for emerging regions (ICTer2012), 2012: IEEE, pp. 182-188.
  • [3] B. Özyurt and M. A. Akçayol, "Fikir Madenciliği ve Duygu Analizi, Yaklaşimlar, Yöntemler Üzerine Bir Araştirma," Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, vol. 6, no. 4, pp. 668-693, 2018.
  • [4] W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093-1113, 2014.
  • [5] A. Singh, N. Thakur, and A. Sharma, "A review of supervised machine learning algorithms," in 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 2016: IEEE, pp. 1310-1315.
  • [6] A. Öztürk, Ü. Durak, and F. Badilli, "Twitter Verilerinden Doğal Dil Işleme Ve Makine Öğrenmesi Ile Hastalik Tespiti," Konya Mühendislik Bilimleri Dergisi, vol. 8, no. 4, pp. 839-852, 2020.
  • [7] H. Çetiner, "Multi-Label Text Analysis With A CNN And LSTM Based Hybrid Deep Learning Model," Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 9, no. 17, pp. 15-16, 2022.
  • [8] A. H. Alamoodi et al., "Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review," Expert systems with applications, vol. 167, p. 114155, 2021.
  • [9] C. R. Machuca, C. Gallardo, and R. M. Toasa, "Twitter sentiment analysis on coronavirus: Machine learning approach," in Journal of Physics: Conference Series, 2021, vol. 1828, no. 1: IOP Publishing, p. 012104.
  • [10] L. Nemes and A. Kiss, "Social media sentiment analysis based on COVID-19," Journal of Information and Telecommunication, vol. 5, no. 1, pp. 1-15, 2021.
  • [11] Ö. Çoban, B. Özyer, and G. T. Özyer, "Sentiment analysis for Turkish Twitter feeds," in 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015: IEEE, pp. 2388-2391.
  • [12] Z. A. Guven, "Comparison of BERT models and machine learning methods for sentiment analysis on Turkish tweets," in 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021: IEEE, pp. 98-101.
  • [13] Y. E. Karaca and S. Aslan, "Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model," Computer Science, no. Special, pp. 366-374, 2021.
  • [14] 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, 2018.
  • [15] H. A. Shehu et al., "Deep sentiment analysis: a case study on stemmed Turkish twitter data," IEEE Access, vol. 9, pp. 56836-56854, 2021.
  • [16] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," CoRR, vol. abs/1810.04805, 2018.
  • [17] J. Ramos, "Using tf-idf to determine word relevance in document queries," in Proceedings of the first instructional conference on machine learning, 2003, vol. 242, no. 1: Citeseer, pp. 29-48.
  • [18] A. I. Kadhim, Y.-N. Cheah, I. A. Hieder, and R. A. Ali, "Improving TF-IDF with singular value decomposition (SVD) for feature extraction on Twitter," in 3rd international engineering conference on developments in civil and computer engineering applications, 2017.
  • [19] D. M. Christopher, R. Prabhakar, and S. Hinrich, "Introduction to information retrieval," ed: Cambridge University Press, 2008.
  • [20] T. Cover and P. Hart, "Nearest neighbor pattern classification," IEEE transactions on information theory, vol. 13, no. 1, pp. 21-27, 1967.
  • [21] L. I. Kuncheva, "On the optimality of Naïve Bayes with dependent binary features," Pattern Recognition Letters, vol. 27, no. 7, pp. 830-837, 2006.
  • [22] V. Vapnik, "Pattern recognition using generalized portrait method," Automation and remote control, vol. 24, pp. 774-780, 1963.
  • [23] A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network," Physica D: Nonlinear Phenomena, vol. 404, p. 132306, 2020.
  • [24] M. V. Valueva, N. Nagornov, P. A. Lyakhov, G. V. Valuev, and N. I. Chervyakov, "Application of the residue number system to reduce hardware costs of the convolutional neural network implementation," Mathematics and computers in simulation, vol. 177, pp. 232-243, 2020.
  • [25] T. N. Sainath, O. Vinyals, A. Senior, and H. Sak, "Convolutional, long short-term memory, fully connected deep neural networks," in 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2015: IEEE, pp. 4580-4584.
  • [26] F. Pedregosa et al., "Scikit-learn: Machine learning in Python," the Journal of machine Learning research, vol. 12, pp. 2825-2830, 2011.
  • [27] M. Abadi et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, 2016.
  • [28] F. Chollet, "keras," ed, 2015.
  • [29] D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.
There are 29 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Mustafa Çataltaş 0000-0002-5598-9659

Büşra Üstünel 0000-0001-5495-9282

Nurdan Akhan Baykan 0000-0002-4289-8889

Publication Date June 1, 2023
Submission Date September 23, 2022
Acceptance Date January 17, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

IEEE M. Çataltaş, B. Üstünel, and N. Akhan Baykan, “SENTIMENT CLASSIFICATION ON TURKISH TWEETS ABOUT COVID-19 USING LSTM NETWORK”, KONJES, vol. 11, no. 2, pp. 341–353, 2023, doi: 10.36306/konjes.1173939.