Efficient Turkish tweet classification system for crisis response

This paper presents a convolutional neural networks Turkish tweet classification system for crisis response. This system has the ability to classify the present information before or during any crisis. In addition, a preprocessing model was also implemented and integrated as a part of the developed system. This paper presents the first ever Turkish tweet dataset for crisis response, which can be widely used and improve similar studies. This dataset has been carefully preprocessed, annotated, and well organized. It is suitable to be used by all the well-known natural language processing tools. Extensive experimental work, using our produced Turkish tweet dataset and the English dataset (“socialmediadisaster-tweets-relevent”), has been performed to illustrate the performance of the developed approach. In addition, vector space model (VSM) techniques were studied to find out the most suitable technique that can be used for the Turkish language. Overall, the developed approach has achieved a quite good performance, robustness, and stability when processing both Turkish and English languages. Our experiments also compare the performance with some stateof-the-art English language systems, such as ”CREES” and ”deep multimodal”.

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[1] Ragini JR, Anand PR. An empirical analysis and classification of crisis related tweets. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC); New York, NY, USA; 2016. pp. 1-4.
[2] Alqaraleh S, Işik M. Comparative analysis of classification techniques for crisis management systems. In: The International Conference of Materials and Engineering Technologies (TICMET19); Gaziantep, Turkey; 2019. pp. 584-592.
[3] Alqaraleh S. Classification of Turkish text using machine learning: a case study using disasters tweets. International Journal of Scientific & Technology Research 2020; 9 (3): 4953-4956.
[4] Kumar A, Singh JP. Location reference identification from tweets during emergencies: a deep learning approach. International Journal of Disaster Risk Reduction 2019; 33: 365-375.
[5] Burel G, Alani H. Crisis event extraction service (CREES) - automatic detection and classification of crisis-related content on social media. In: ISCRAM - 15th International Conference on Information Systems for Crisis Response and Management; New York, NY, USA; 2018. pp. 597-608.
[6] Kumar A, Singh JP, Dwivedi YK, Rana NP. A deep multi-modal neural network for informative Twitter content classification during emergencies. Annals of Operations Research 2020; 1: 1-32.
[7] Baygın M. Classification of text documents based on naive Bayes using N-Gram features. In: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP); Malatya, Turkey; 2018. pp. 1-5.
[8] Kılınç D. The effect of ensemble learning models on Turkish text classification. Celal Bayar Üniversitesi Fen Bilimleri Dergisi 2016; 12 (2): 14-25.
[9] Kilimci ZH, Akyokus S. Deep learning-and word embedding-based heterogeneous classifier ensembles for text classification. Complexity 2018; 2018: 1-20.
[10] Kilimci ZH, Akyokuş S. The evaluation of word embedding models and deep learning algorithms for Turkish text classification. In: 2019 4th International Conference on Computer Science and Engineering (UBMK); Samsun, Turkey; 2019. pp. 548-553.
[11] Aydoğan M, Karci A. Improving the accuracy using pre-trained word embeddings on deep neural networks for Turkish text classification. Physica A: Statistical Mechanics and Its Applications 2020; 1: 541.
[12] Perkins J. Python 3 Text Processing with NLTK 3 Cookbook. USA: Packt Publishing Ltd, 2014.
[13] Christian H, Agus MP, Suhartono D. Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF). ComTech: Computer, Mathematics and Engineering Applications 2016; 7 (4): 285- 294.
[14] Li H, Caragea D, Li X, Caragea C. Comparison of word embeddings and sentence encodings as generalized representations for crisis Tweet classification tasks. In: ISCRAM Asian Pacific Conference; Wellington, New Zealand; 2018. pp. 1-13.
[15] Liu Q, Kusner MJ, Blunsom P. A survey on contextual embeddings. ArXiv 2020; arXiv:2003.07278 [cs.CL].
[16] Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP); Doha, Qatar; 2014. pp. 1532-1543.
[17] Joulin A, Grave E, Bojanowski P, Douze M, Jégou H et al. FastText.zip: Compressing text classification models. ArXiv 2016; arXiv:1612.03651 [cs.CL].
[18] Şahin G. Turkish document classification based on Word2Vec and SVM classifier. In: 25th Signal Processing and Communications Applications Conference (SIU); Antalya, Turkey; 2017. pp. 1-4.
[19] O’Keefe SE, Alrashdi RM. Deep learning and word embeddings for tweet classification for crisis response. In: The 3rd National Computing Colleges Conference; Abha, Saudi Arabia; 2018. pp.1-20.
[20] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 2014; 15 (1): 1929-1958.
[21] Khan SH, Hayat M, Porikli F. Regularization of deep neural networks with spectral dropout. Neural Networks 2019; 110: 82-90.
[22] Ramachandran P, Zoph B, Le QV. Searching for activation functions. In: Sixth International Conference on Learning Representations; Vancouver, Canada; 2018. pp.1-20.
[23] Adali K, Eryiğit G. Vowel and diacritic restoration for social media texts. In: Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM); Gothenburg, Sweden; 2014. pp. 53-61.
[24] Eryiğit G, Adalı E. An affix stripping morphological analyzer for Turkish. In: Proceedings of the IASTED international conference artificial intelligence and applications; Innsbruck, Austria; 2004. pp. 299-304.
[25] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research 2011; 12: 2825-2830.
[26] Grave E, Bojanowski P, Gupta P, Joulin A, Mikolov T. Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018); Miyazaki, Japan; 2018. pp. 3483-3487.