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Research Article

Bi-Directional CNN-RNN Architecture with Group-Wise Enhancement and Attention Mechanisms for Cryptocurrency Sentiment Analysis

, ORCID Icon &
Article: 2145641 | Received 31 Jul 2022, Accepted 04 Nov 2022, Published online: 13 Nov 2022

References

  • Abraham, J., D. Higdon, J. Nelson, and J. Ibarra. 2018. Cryptocurrency price prediction using Tweet volumes and sentiment analysis. SMU Data Science Review 1 (3):1–3472.
  • Aslam, N., F. Rustam, E. Lee, P. B. Washington, and I. Ashraf. 2022. Sentiment analysis and emotion detection on cryptocurrency related tweets using ensemble LSTM-GRU model. IEEE Access 10:39313–24. doi:10.1109/ACCESS.2022.3165621.
  • Basiri, M. E., S. Nemati, M. Abdar, E. Cambria, and U. R. Acharya. 2021. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems 115:279–94. doi:10.1016/j.future.2020.08.005.
  • Calefato, F., F. Lanubile, and N. Novielli. 2017. EmoTxt: A toolkit for emotion recognition from text. Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, 79–80, San Antonio, TX, USA, 2017 October.
  • Chatterjee, A., U. Gupta, M. K. Chinnakotla, R. Srikanth, M. Galley, and P. Agrawal. 2019. Understanding emotions in text using deep learning and big data. Computers in Human Behavior 93:309–17. doi:10.1016/j.chb.2018.12.029.
  • Cho, K., B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation ArXiv 2014: 1–15 . .
  • Chuen, D. L. K., L. Guo, and Y. Wang. 2017. Cryptocurrency: A new investment opportunity? The Journal of Alternative Investments 20 (3):16–40. doi:10.3905/jai.2018.20.3.016.
  • Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling ArXiv 2014: 1–9.
  • Colianni, S., S. Rosales, and M. Signorotti. 2015. Algorithmic trading of cryptocurrency based on twitter sentiment analysis. CS229 Project, Stanford University 1 (5):1–4.
  • Çılgın, C., C. Ünal, S. Alıcı, E. Akkol, and Y. Gökşen. 2020. Metin Sınıflandırmada Yapay Sinir Ağları ile Bitcoin Fiyatları ve Sosyal Medyadaki Beklentilerin Analizi. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 4 (1):106–26. doi:10.31200/makuubd.651904.
  • Di Pierro, M. 2017. What is the blockchain? Computing in Science & Engineering 19 (5):92–95. doi:10.1109/MCSE.2017.3421554.
  • Go, A., R. Bhayani, and L. Huang. 2009. Twitter sentiment classification using distant supervision. CS224N Project, Stanford University 1 (12):1–6.
  • Gutiérrez, G., J. Canul-Reich, A. O. Zezzatti, L. Margain, and J. Ponce. 2018. Mining: Students comments about teacher performance assessment using machine learning algorithms. International Journal of Combinatorial Optimization Problems and Informatics 9 (3):26–40.
  • Haryadi, D., and G. P. Kusuma. 2019. Emotion detection in text using nested long short-term memory. International Journal of Advanced Computer Science and Applications 10 (6):351–57. doi:10.14569/IJACSA.2019.0100645.
  • Hasan, M., E. Rundensteiner, and E. Agu. 2019. Automatic emotion detection in text streams by analyzing twitter data. International Journal of Data Science and Analytics 7 (1):35–51. doi:10.1007/s41060-018-0096-z.
  • Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
  • Huang, X., W. Zhang, X. Tang, M. Zhang, J. Surbiryala, V. Iosifidis, Z. Liu, and J. Zhang. 2021. LSTM based sentiment analysis for cryptocurrency prediction ArXiv 2021: 1–4.
  • Kamal, A., and M. Abulaish. 2022. CAT-BiGRU: Convolution and attention with bi-directional gated recurrent unit for self-deprecating sarcasm detection. Cognitive computation 14 (1):91–109. doi:10.1007/s12559-021-09821-0.
  • Köksal, B., G. Erdem, C. Türkeli, and Z. K. Öztürk. 2021. Twitter’da Duygu Analizi Yöntemi Kullanılarak Bitcoin Değer Tahminlemesi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 (3):280–97. doi:10.29130/dubited.792909.
  • Lamon, C., E. Nielsen, and E. Redondo. 2017. Cryptocurrency price prediction using news and social media sentiment. SMU Data Science Review 1 (3):1–22.
  • Li, X., X. Hu, and J. Yang. 2019. Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. ArXiv Preprint ArXiv:1905.09646.
  • Li, J., M. T. Luong, D. Jurafsky, and E. Hovy. 2015. When are tree structures necessary for deep learning of representations? ArXiv Preprint ArXiv:1503.00185.
  • Liu, G., and J. Guo. 2019. Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing 337:325–38. doi:10.1016/j.neucom.2019.01.078.
  • Liu, Y., L. Ji, R. Huang, T. Ming, C. Gao, and J. Zhang. 2019. An attention-gated convolutional neural network for sentence classification. Intelligent Data Analysis 23 (5):1091–107. doi:10.3233/IDA-184311.
  • Li, X., and X. Wu. 2015. Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4520–24, South Brisbane, QLD, Australia, 2015 April.
  • Mehta, T., G. Kolase, V. Tekade, R. Sathe, and A. Dhawale. 2020. Price prediction and analysis of financial markets based on news social feed and sentiment index using machine learning and market data. International Research Journal of Engineering and Technology Technology 7 (6):483–89.
  • Mikolov, T., I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed representations of words and phrases and their compositionality Advances in Neural Information Processing Systems 26 3111–3119.
  • Onan, A. 2021. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurrency and Computation: Practice and Experience 33 (23):e5909. doi:10.1002/cpe.5909.
  • Onan, A. 2022. Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. Journal of King Saud University-Computer and Information Sciences 34 (5):2098–117. doi:10.1016/j.jksuci.2022.02.025.
  • Onan, A., and M. A. Toçoğlu. 2021. Weighted word embeddings and clustering‐based identification of question topics in MOOC discussion forum posts. Computer Applications in Engineering Education 29 (4):675–89. doi:10.1002/cae.22252.
  • Pant, D. R., P. Neupane, A. Poudel, A. K. Pokhrel, and B. K. Lama. 2018. Recurrent neural network based bitcoin price prediction by twitter sentiment analysis. Proceedings of the IEEE 3rd International Conference on Computing, Communication and Security, Kathmandu, Nepal, 128–32, 2018 October.
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, and B. Thirion. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12:2825–30.
  • Raju, S. M., and A. M. Tarif. 2020. Real-time prediction of BITCOIN price using machine learning techniques and public sentiment analysis ArXiv 2020 1–14.
  • Rasool, A., Q. Jiang, Q. Qu, and C. Ji. 2021. WRS: A novel word-embedding method for real-time sentiment with integrated LSTM-CNN model. Proceedings of the 2021 IEEE International Conference on Real-time Computing and Robotics, 590–95, Xining, China, 2021 July.
  • Rezaeinia, S. M., R. Rahmani, A. Ghodsi, and H. Veisi. 2019. Sentiment analysis based on improved pre-trained word embeddings. Expert Systems with Applications 117:139–47. doi:10.1016/j.eswa.2018.08.044.
  • Rojas‐barahona, L. M. 2016. Deep learning for sentiment analysis. Language and Linguistics Compass 10 (12):701–19. doi:10.1111/lnc3.12228.
  • Salam, S. A., and R. Gupta. 2018. Emotion detection and recognition from text using machine learning. International Journal of Computer Science and Engineering 6 (6):341–45. doi:10.26438/ijcse/v6i6.341345.
  • Shah, F. M., A. S. Reyadh, A. I. Shaafi, S. Ahmed, and F. T. Sithil. 2019. “Emotion detection from tweets using AIT-2018 dataset.” Proceedings of the 5th International Conference on Advances in Electrical Engineering, Dhaka, Bangladesh, 2019 September: 575–80.
  • Şaşmaz, E., and F. B. Tek. 2021. Tweet sentiment analysis for cryptocurrencies. Proceedings of the 6th International Conference on Computer Science and Engineering, 613–18, Ankara, Turkey, 2021 September.
  • Tikhomirov, S., E. Voskresenskaya, I. Ivanitskiy, R. Takhaviev, E. Marchenko, and Y. Alexandrov. 2018. Smartcheck: Static analysis of ethereum smart contracts. Proceedings of the 1st International Workshop on Emerging Trends in Software Engineering for Blockchain, Gothenburg, Sweden 2018 May: 9–16.
  • Tocoglu, M. A., O. Ozturkmenoglu, and A. Alpkocak. 2019. Emotion analysis from Turkish tweets using deep neural networks. IEEE Access 7:183061–69. doi:10.1109/ACCESS.2019.2960113.
  • Usama, M., B. Ahmad, E. Song, M. S. Hossain, M. Alrashoud, and G. Muhammad. 2020. Attention-based sentiment analysis using convolutional and recurrent neural network. Future Generation Computer Systems 113:571–78. doi:10.1016/j.future.2020.07.022.
  • Valencia, F., A. Gómez-Espinosa, and B. Valdés-Aguirre. 2019. Price movement prediction of cryptocurrencies using sentiment analysis and machine learning. Entropy 21 (6):589. doi:10.3390/e21060589.
  • Wang, J., L. C. Yu, K. R. Lai, and X. Zhang. 2016. “Dimensional sentiment analysis using a regional CNN-LSTM model.” Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany 2016 August: 225–30.
  • Wen, S., and J. Li. 2018. “Recurrent convolutional neural network with attention for twitter and yelp sentiment classification: ARC model for sentiment classification.” Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, Sanya, Hainan, China 2018 December: 1–7.
  • Wołk, K. 2020. Advanced social media sentiment analysis for short-term cryptocurrency price prediction. Expert Systems 37 (2):e12493. doi:10.1111/exsy.12493.
  • Wöhrer, M., and U. Zdun. 2018. “Design patterns for smart contracts in the ethereum ecosystem.” Proceedings of the 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data, Halifax, NS, Canada 2018 July:1513–20.
  • Yang, Z., D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. 2016. Hierarchical Attention networks for document classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA 2016 June: 1480–89.
  • Zhang, X., W. Li, H. Ying, F. Li, S. Tang, and S. Lu. 2020. Emotion detection in online social networks: a multilabel learning approach. IEEE Internet of Things Journal 7 (9):8133–43. doi:10.1109/JIOT.2020.3004376.
  • Zhu, X., P. Sobhani, and H. Guo. 2015. Long short-term memory over tree structures. ArXiv Preprint ArXiv:1503.04881.