A Multi-label Classification of Disaster-Related Tweets with Enhanced Word Embedding Ensemble Convolutional Neural Network Model
Olteanu A, Vieweg S, Castillo C. What to expect when the unexpected happens: Social media communications across crises. In: Proceedings of the 18th ACM conference on computer-supported cooperative work & social computing. ACM; 2015. p. 994–1009.
Gralla E, Goentzel J, Van de Walle B. Understanding the information needs of field-based decision-makers in humanitarian response to sudden-onset disasters. In: ISCRAM; 2015.
Y. Agarwal, D. K. Sharma and R. Katarya, "Sentiment/Opinion Review Analysis: Detecting Spams from the good ones! ," 2019, 4th International Conference on Information Systems and Computer Networks (ISCON), 2019, pp. 557-563, http://doi.org/10.1109/ISCON47742.2019.9036249
Imon Banerjee, Yuan Ling, Matthew C. Chen, Sadid A. Hasan, Curtis P. Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L. Rubin, Oladimeji Farri, Matthew P. Lungren, "Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification," in Artificial Intelligence in Medicine, Volume 97, 2019, Pages 79-88, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2018.11.004
Manzhu Yu, Qunying Huang, Han Qin, Chris Scheele & Chaowei Yang, "Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies," in International Journal of Digital Earth, 2019, Pages 1230-1247, http://doi.org/10.1080/17538947.2019.1574316.
M. P. Akhter, Z. Jiangbin, I. R. Naqvi, M. Abdelmajeed, A. Mehmood and M. T. Sadiq, "Document-Level Text Classification Using Single-Layer Multisize Filters Convolutional Neural Network," in IEEE Access, vol. 8, pp. 42689-42707, 2020, http://doi.org/10.1109/ACCESS.2020.2976744.
H. Ma, Y. Li, X. Ji, J. Han and Z. Li, "MsCoa: Multi-Step Co-Attention Model for Multi-Label Classification," in IEEE Access, vol. 7, pp. 109635-109645, 2019, http://doi.org/10.1109/ACCESS.2019.2933042 .
Alsaedi, N., Burnap, P. and Rana, O. (2017). Can We Predict a Riot? Disruptive Event Detection Using Twitter. ACMTransactions on Internet Technology, 17(2), 18.
Starbird, K., Palen, L. Hughes, A.L. and Vieweg, S. (2010). Chatter on the red: what hazards threat reveals about the social life of microblogged information. Proceedings of CSCW. ACM, 241–250.
Vieweg, S., Castillo, C. and Imran, M. (2014). Integrating social media communications into the rapid assessment of sudden onset disasters. In Social Informatics, Springer, 444–461.
Verma, S., Vieweg, S., Corvey, W., Palen, L., Martin, J., Palmer, M., Schram, A., & Anderson, K. (2021). Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 385-392. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14119.
Imran, M., Castillo, C., Lucas, J., Meier, P., Vieweg, S., (2014). Aidr: Artificial intelligence for disaster response, in: Proceedings of the 23rd International Conference on World Wide Web, ACM. pp. 159–162 .
Sreenivasulu, M., Sridevi, M., (2017). Mining informative words from the tweets for detecting the resources during disaster, in: International Conference on Mining Intelligence and Knowledge Exploration, Springer. pp. 348–358.
Rudra, K., Ganguly, N., Goyal, P., Ghosh, S., (2018). Extracting and summarizing situational information from the twitter social media during disasters. ACM Transactions on the Web (TWEB) 12, 17.
Rudra, K., Ghosh, S., Ganguly, N., Goyal, P., Ghosh, S., (2015). Extracting situational information from microblogs during disaster events: a classification-summarization approach, in: Proceedings of the 24th ACM International on Conference on Information and Knowledge Man-agement, ACM. pp. 583–59 .
Caragea, C., Silvescu, A., Tapia, A.H., (2016). Identifying informative messages in disaster events using convolutional neural networks, in: International Conference on Information Systems for Crisis Response and Management.
Nguyen, T.D., Al-Mannai, K., Joty, S.R., Sajjad, H., Imran, M., & Mitra, P. (2016). Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks. ArXiv, abs/1608.03902.
Nguyen, D.T., Al Mannai, K.A., Joty, S., Sajjad, H., Imran, M., Mitra, P., (2017). Robust classification of crisis-related data on social networks using convolutional neural networks, in: Eleventh International AAAI Conference on Web and Social Media.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., (2013). Distributed representations of words and phrases and their compositionality, in: Advances in neural information processing systems, pp. 3111–3119.
Madichetty, S., Sridevi, M., (2019). Detecting informative tweets during disaster using deep neural networks, in: 2019 11th International Conference on Communication Systems & Networks (COMSNETS), IEEE. pp. 709–713.
Rodrigues, A.P., Chiplunkar, N.N., "A new big data approach for topic classification and sentiment analysis of Twitter data", Evol. Intel. (2019). https://doi.org/10.1007/s12065-019-00236-3.
M. Bouazizi and T. Ohtsuki, "A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter," in IEEE Access, vol. 5, pp. 20617-20639, 2017, http://doi.org/10.1109/ACCESS.2017.2740982.
Sreenivasulu, Madichetty, Sridevi, "Improved Classification of Crisis-Related Data on Twitter using Contextual Representations," in Procedia Computer Science, Volume 167, 2020, Pages 962-968, https://doi.org/10.1016/j.procs.2020.03.395.
Janjua, S. H., Siddiqui, G. F., Sindhu, M. A., & Rashid, U. (2021). Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning. PeerJ. Computer science, 7, e433. https://doi.org/10.7717/peerj-cs.433.
Krawczyk B., McInnes B.T., Cano A. (2017) Sentiment Classification from Multi-class Imbalanced Twitter Data Using Binarization. In: Martínez de Pisón F., Urraca R., Quintián H., Corchado E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science, vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_3.
A. Olteanu, C. Castillo, F. Diaz, and S. Vieweg, "Crisislex: A lexicon for collecting and filtering microblogged communi-cations in crises," in In Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (ICWSM" 14), no. EPFL-CONF-203561, 2014.
L.Mou and X. X. Zhu, "Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 110-122, Jan. 2020, http://doi.org/10.1109/TGRS.2019.2933609.
Y. Sun, B. Xue, M. Zhang, G. G. Yen and J. Lv, "Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification," in IEEE Transactions on Cybernetics, vol. 50, no. 9, pp. 3840-3854, Sept. 2020, http://doi.org/10.1109/TCYB.2020.2983860.
Cui, Renhao & Agrawal, Gagan & Ramnath, Rajiv. (2020). Tweets can tell: activity recognition using hybrid gated recurrent neural networks. Social Network Analysis and Mining. 10. 10.1007/s13278-020-0628.
D. Hu and B. Krishnamachari, "Fast and Accurate Streaming CNN Inference via Communication Compression on the Edge," 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI), 2020, pp. 157-163, http://doi.org/10.1109/IoTDI49375.2020.00023.
Shin, Joongbo & Kim, Yanghoon & Yoon, Seunghyun & Jung, Kyomin. (2018). Contextual-CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification. 2018 IEEE International Conference on Big Data and Smart Computing491-494. 10.1109/BigComp.2018.00079.
Lan, Yangyang & Hao, Yazhou & Xia, Kui & Qian, Buyue & Li, Chen. (2020). Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2987101.
HaCohen-Kerner Y, Miller D, Yigal Y. 2020. The influence of preprocessing on text classification using a bag-of-words representation. PLOS ONE 15(5):e0232525,DOI 10.1371/journal.pone.0232525.
José-Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michał Woźniak, Salvador García, "Monotonic classification: An overview on algorithms," in performance measures and data sets, Neurocomputing, Volume 341, 2019, Pages 168-182, ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2019.02.024
This work is licensed under a Creative Commons Attribution 3.0 License.