Open AccessDisaster Sentiment Analysis: Addressing the Challenges of Decision-Makers in Visualizing Netizen Tweets
Author(s) -
R. Bäro,
Thelma D. Palaoag
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/803/1/012039
Subject(s) - visualization , data science , computer science , sentiment analysis , subjectivity , python (programming language) , emergency management , government (linguistics) , decision support system , knowledge management , political science , data mining , artificial intelligence , philosophy , linguistics , epistemology , law , operating system
Among 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.