Abstract
Microblogs generally contribute to statements that are made in public by the users. Tweets made on the Twitter platform fall in the microblog category. Microblogging sites have become important source in disaster events. In this paper, we compare various algorithms to study the effectiveness of retrieval and classification of tweets that were collected during disasters. The overall goal is to identify the relief work and retrieve it efficiently from the microblog tweets. The evaluation metrics that were used are precision, recall and F-score. We have observed that support vector machine (SVM) has the highest accuracy in classification of tweets based on pre-defined retrieval criteria.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Twitter Homepage. https://twitter.com/home. Last Accessed on 2019 Dec 5
FIRE Microblog Track. https://sites.google.com/site/fire2016microblogtrack. Last Accessed 2019 Dec 5
IRMiDis homepage, Information Retrieval from Microblogs during Disasters (IRMiDis). https://sites.google.com/site/irmidisfire2017/. Last Accessed on 2019 Dec 5
TREC Homepage. https://trec.nist.gov/. Last Accessed on 2019 Dec 5
Lin J, Efron M, Wang Y, Sherman G, Voorhees E (2018) Overview of the TREC-2015 microblog track
Basu M, Roy A, Ghosh K, Bandyopadhyay S, Ghosh S (2017) Micro-blog retrieval in a disaster situation: a new test collection for evaluation. In: SMERP, ECIR
Strohman T, Metzler D, Turtle H, Croft WB (2004) Indri: a language model-based search engine for complex queries. In: Proceedings ICIA
Ghosh S et al (2017) First international workshop on exploitation of social media for emergency relief and preparedness (SMERP). In: Advances in information retrieval, ECIR 2017, vol 10193
Mikonos T, Yih W, Zweig G (2013) Linguistic regularities in continuous space word representation. In: NAACL HLT
Ponte JM, Croft WB (1998) A language modeling approach to information retrieval. In: Proc. ACM SIGIR, pp 275–281
Baeza-Yates R, Ribeiro Neto B (2011) Modern Information Retrieval, 2nd edn. Pearson Publication
Acknowledgements
We would like to extend a token of gratitude to Dr. Vikram Kaushik for being the mentor and giving continuous guidance, support and motivation in this research work. We also acknowledge organizers of Microblog Track of FIRE-2016 to provide the dataset for research. We would also like to acknowledge the Ministry of Information Technology (MeitY), New Delhi, for partial funding towards this project and Sankalchand Patel University, Visnagar, for providing infrastructure resources.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prajapati, H., Raval, H., Joshi, H. (2021). A Comparative Study of Classification Techniques in Context of Microblogs Posted During Natural Disaster. In: Kotecha, K., Piuri, V., Shah, H., Patel, R. (eds) Data Science and Intelligent Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 52. Springer, Singapore. https://doi.org/10.1007/978-981-15-4474-3_8
Download citation
DOI: https://doi.org/10.1007/978-981-15-4474-3_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4473-6
Online ISBN: 978-981-15-4474-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)