Extracting Information from Microblogs Posted During Natural Disasters
Santosh Kumar Vishwakarma1, Rishabh Singh Chandel2, Parul Hora3
1Dr. Santosh K. Vishwakarma*, is Working as Associate Professor in the Department of CSE, School of Computing & IT, Manipal University Jaipur.
2Rishabh Singh Chandel, Master’s Student in Computer Science & Engineering from Gyan Ganga Institute of Technology and Sciences, Jabalpur.
3Parul Hora, Masters Student in Computer Science & Engineering Department of Gyan Ganga Institute of Technology and Sciences, Jabalpur.
Manuscript received on January 11, 2020. | Revised Manuscript received on January 21, 2020. | Manuscript published on February 10, 2020. | PP: 878-782 | Volume-9 Issue-4, February 2020. | Retrieval Number: C9109019320 /2020©BEIESP | DOI: 10.35940/ijitee.C9109.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social Networking websites plays an important role in our life. The usage of the above websites is in every domain of our lives and they have increasingly infused itself into daily life. In recent years, the social networking websites such as Twitter, Facebook, are used in natural disasters. Many features have been included in Twitter for fast responses in such kind of unexpected events. This paper is based on the experiments performed on the 2017 Microblog Track provided by Forum of Information Retrieval & Evaluation. The Classification schemes are used with two predefined labels as need and availability. The various pre-processing and natural language processing techniques are applied before the training of the model. The experiments showed that the classification accuracy is increased when the term weight is modified by using the information gain method and using the SVM classifier. This system automatically annotated the FIRE-2015 dataset of microblog track with 97% accuracy.
Keywords: Classification, NLP, FIRE, Information Retrieval, tweets; Natural Disaster; Social Media, disaster monitoring, text classifier Microblogging sites, Twitter, Precision, Recal
Scope of the Article: Classification