Disaster sentiment analysis: addressing the challenges of decision-makers in visualizing netizen tweets

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dc.contributor.authorBaro, R. A.
dc.contributor.authorPalaoag, T. D.
dc.date.accessioned2026-08-19T02:55:43Z
dc.date.available2026-08-19T02:55:43Z
dc.date.issued2020
dc.descriptionFull text.
dc.description.abstractAmong other crucial aspects of disaster-related initiatives is decision-making. The capacity to carry out efficient holistic management of measures and programs rely greatly upon the decision-makers and the religious participation of all stakeholders. Adopting the sentiments can help government leaders in their decision-making responsibilities towards disaster management when meaningful patterns are easily visualized. The purpose of this research is to analyze disaster-related sentiments from Twitter and presents the results using data visualization easily understood by decision-makers. The study underscores the use of Python programming with NLP techniques to learn from Twitter data, Sentiment Analysis using TextBlob to identify polarity and subjectivity of tweets and Plotly for interactive data visualization. The implementation of the study contributes to the reduction of injury, damage to infrastructures, properties and especially the loss of life. The challenges of both decision-makers and data scientists working in the discipline are highlighted in the study. © Published under licence by IOP Publishing Ltd.
dc.identifier.citationBaro R.A., & Palaoag T.D. (2020). Disaster sentiment analysis: addressing the challenges of decision-makers in visualizing netizen tweets. IOP Conference Series: Materials Science and Engineering, 803(1). https://doi.org/10.1088/1757-899X/803/1/012039
dc.identifier.issn1757-8981
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2379
dc.language.isoen
dc.publisherInstitute of Physics Publishing
dc.sdgSDG 11
dc.titleDisaster sentiment analysis: addressing the challenges of decision-makers in visualizing netizen tweets
dc.typeArticle
oaire.citation.issue1
oaire.citation.volume803
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