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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2017 Vol.7(6): 194-197 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.6.645

Corpus Analysis of Earthquake Related Tweets through Topic Modelling

Lany L. Maceda, Jennifer L. Llovido, and Thelma D. Palaoag
Abstract—The Philippine archipelago is one of the most disaster-prone area due to its location. As Twitter grows everyday, it has now become a valuable source of people’s opinion. The main purpose of the study is to use Twitter, as text corpora in the attainment of disaster risk reduction. Earthquake related tweets posted from July 1, 2017 to August 31, 2017 were programmatically collected. Data cleaning was made by removing noisy words, hence, from 90,692 collected tweets this resulted to 41,500 cleaned tweets. Topic modeling identifies patterns in a corpus. Findings revealed that there is a need to strengthen the conduct of earthquake drill and early warning notices to lessen the vulnerability of people and property, and reduce the causal damages of earthquake, thus improve its community resiliency. Topic results shall be discussed with concerned agencies for its possible consideration and inclusion to enhance the earthquake-related disaster management plan.

Index Terms—Topic modeling, corpus analysis, tweets, earthquake.

Lany L. Maceda and Jennifer L. Llovido are with the Bicol University College of Science, Computer Science and Information Technology Department, Legazpi City, Philippines (e-mail: lanylm@yahoo.com, jllovido@gmail.com).
Dr. Thelma D. Palaoag is with the University of the Cordilleras College of Information Technology and Computer Science, Baguio City, Philippines (e-mail: tpalaoag@gmail.com).


Cite: Lany L. Maceda, Jennifer L. Llovido, and Thelma D. Palaoag, "Corpus Analysis of Earthquake Related Tweets through Topic Modelling," International Journal of Machine Learning and Computing vol. 7, no. 6, pp. 194-197, 2017.

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