Emergency Tweet Categorization and Prioritization
DOI:
https://doi.org/10.13052/jmm1550-4646.21347Keywords:
Natural language processing, undersampling, oversampling, real-time monitoring, BERTAbstract
The “Emergency Tweet Categorization and Prioritization System” is an all-encompassing solution aimed at reducing the workload of emergency service departments by classifying and prioritizing tweets. The system utilizes Google’s BERT model to increase the effectiveness of the rescue process. In natural language processing and text classification, numerous projects have attempted tweet categorization; however, our project specifically addresses prioritizing tweets for emergency departments. The BERT model is optimized to distinguish the authenticity of the tweets and label the tweets which are related to the fire department, police, and medicals. The train dataset has been balanced through the process of oversampling and undersampling to be able to run smoothly and highly accurately. The model has been able to post an 87% accuracy ratio in predicting authenticity. Some of the future directions include time- and distance-based prioritization of tweets, expanding the system to emergency calls, and designing real-time monitoring and alerting mechanisms.
Downloads
References
L. Hong et al., “Towards understanding communication behavior changes during floods using cell phone Data” in Lect. Notes Comput. Sci. Soc. Info 2018, S. Staab, O. Koltsova and D. Ignatov, Eds. So-cial Informatics, 2018(), vol. 11186. Springer, Cham
S. Skogevall et al., “Telephone nurses’ perceived stress, self-efficacy and empathy in their work with frequent callers,” Nurs. Open, vol. 9, no. 2, pp. 1394–1401, 2022 Mar. doi: 10.1002/nop2.889. PMID:34528768; PMCID:PMC8859069.
Lai, S. Xu et al., “Recurrent convolutional neural networks for text classification,” Proc. AAAI Conference on Artificial Intelligence, vol. 29, no. 1, 2015.
B. Mocanu et al.“ODIN IVR-Interactive Solution for Emergency Calls Handling” Appl. Sci., vol. 12, no. 21, p. 10844, 2022. doi: 10.3390/app122110844.
C. Castillo et al., “Information credibility on twitter” in Proc. 20th International Conference on World Wide Web (WWW ’11). New York, NY, USA: Association for Computing Machinery, 2011, pp. 675–684. doi: 10.1145/1963405.1963500.
H. Liang et al, “Text feature extraction based on deep learning: A review,” EURASIP J. Wirel. Commun. Netw., vol. 2017, no. 1, p. 211, 2017. doi: 10.1186/s13638-017-0993-1.
Y. Kim, “Convolutional neural networks for sentence classification” in Proc. 2014 Conference on Empirical Methods in Natural Language Processing, 2014, 1746–1751. doi: 10.3115/v1/D14-1181.
J. Laksana and A. Purwarianti, Indonesian Twitter Text Authority Classification for Government in Bandung, 2014, pp. 129–134. doi: 10.1109/ICAICTA.2014.7005928.
Y. Li et al., “Extracting features from requirements: Achieving accuracy and automation with neural networks,” IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), Campobasso, Italy, 2018, 2018, pp. 477–481. doi: 10.1109/SANER.2018.8330243.
A. Srivastava, S. Umrao, S. Biswas, R. dubey, and M. I. Zafar, “FCCC: Forest Cover Change Calculator User Interface for Identifying Fire Incidents in Forest Region using Satellite Data,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 7, pp. 948–959, 2023, doi: 10.14569/IJACSA.2023.01407103.
S. Lai et al., “Recurrent convolutional neural networks for text classification,” AAAI, vol. 29, no. 1, 2015. doi: 10.1609/aaai.v29i1.9513.
Srivastava, A. 2024. Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine. Geodesy and Cartography. 50, 4 (Dec. 2024), 162–178. doi: https://doi.org/10.3846/gac.2024.20106.
A. Vaswani et al., “Attention is all you need,” Arxiv. /abs/1706.03762, 2017.
A. Çelıkten and H. Bulut, “Turkish medical text classification using BERT,” 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, vol. 2021, 2021, pp. 1–4, doi: 10.1109/SIU53274.2021.9477847.
A. Murarka et al., “Classification of mental illnesses on social media using Roberta” in Proc. 12th International Workshop on Health Text Mining and Information Analysis, 2021, pp. 59–68.
S. Prabhu et al., “Multi-class text classification using BERT-based active learning,” Arxiv./abs/2104.14289, 2021.
A. Çelıkten and H. Bulut, “Turkish medical text classification using BERT,” 29th Signal Processing and Communications Applications Conference (SIU), Istanbul, Turkey, vol. 2021, 2021, pp. 1–4, doi: 10.1109/SIU53274.2021.9477847.
A. Srivastava and S. Biswas, “Analyzing Land Cover Changes over Landsat-7 Data using Google Earth Engine,” Proc. 3rd Int. Conf. Artif. Intell. Smart Energy, ICAIS 2023, pp. 1228–1233, 2023, doi: 10.1109/ICAIS56108.2023.10073795.
D. Warner et al., “Characterization of call prioritisation time in a Police Priority Dispatch System,” Annals Emerg. Dispatch Resp., vol. 2, no. 2, pp. 16–22, 2014.
Sharma, Himanshu, Prabhat Kumar, and Kavita Sharma. “Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT.” Scalable Computing: Practice and Experience 25.5 (2024): 3778–3795.
A. Srivastava, R. Dubey, and S. Biswas, “Comparison of Sentinel and Landsat Data Sets over Lucknow Region Using Gradient Tree Boost Supervised Classifier,” Lect. Notes Networks Syst., vol. 730 LNNS, pp. 221–232, 2023, doi: 10.1007/978-981-99-3963-3_18.
Sharma, H., Kumar, P., and Sharma, K. (2025). Advanced Security for IoT and Smart Devices: Addressing Modern Threats and Solutions. Emerging Threats and Countermeasures in Cybersecurity, 191–216.
H.J. Kim, Feature Extraction of Text for Deep Learning Algorithms: Application on Fake News Dectection, 2020.
S. Skogevall et al., “A survey of telephone nurses’ experiences in their encounters with frequent callers,” J. Adv. Nurs., vol. 76, no. 4, pp. 1019–1026, 2020 Apr. doi: 10.1111/jan.14308:1111/jan.14308. PMID:31997365.
