Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Huizinga, Tylor; Ayanso, Anteneh; Smoor, Miranda; T, Wronski

doi:10.4018/ijban.2017010101

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Barnes et al also identified research gaps, for example, to analyze the relationship between consumer buying behavior and SC deficiencies and opportunities around the reduced LOS perceived by consumers ( 4 ). Text analytics is a methodology that is widely used in natural disaster analysis ( 32 ), for instance, Huizinga et al analyzed the relationship between natural disasters and insurance by using around 19,000 tweets on more than 11,000 natural disasters ( 33 ). Goh and Sun studied citizens’ opinions and discovered social insights into natural disasters, which are vital for policy makers and for business decisions ( 34 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Precautionary and Opportunistic Buying Behaviors and Supply Issues on Supply Chain Resilience During the COVID-19 Pandemic

Rivera-Royero

Jaller

Jenn

2022

Transportation Research Record: Journal of the Transportation R

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This paper proposes a text analytics approach using dictionary-based clustering, word counting data, and discrete regression modeling to study the relationship between demand behaviors and supply issues affecting supply chain resilience during the first stages of the COVID-19 pandemic. The work used news and media articles gathered from the LexisNexis database covering 5 months between February and July 2020. The data analyses describe the general patterns in the news texts by using text mining techniques, and the methodology describes the relationship between consumer behavior, supply chain issues, and the reduced level of service shown during the study period. Demand behaviors include precautionary and opportunistic buying, which affected many countries and could be the result of a lack of perceived control and other factors; for example, near-empty shelves of certain products could have prompted consumers to increasingly look for comparable products, driving up demand. Additionally, the method explored the potential effect of strategies to mitigate impacts on the resilience of supply chains. The results confirmed that buying behaviors and a reduction in the capacity of the supply chain led to a lower level of service being perceived by consumers, however, resilience strategies were found to mitigate the impact of such capacity reductions. Empirical analyses showed that the proposed approach, using data extracted from the news, could identify and represent impacts consistent with expectations from the supply chain field under disruptions, and quantify the magnitude of the impacts as the pandemic evolved, providing more information for decision making.

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Section: Literature Reviewmentioning

confidence: 99%

Impacts of Precautionary and Opportunistic Buying Behaviors and Supply Issues on Supply Chain Resilience During the COVID-19 Pandemic

Rivera-Royero

Jaller

Jenn

2022

Transportation Research Record: Journal of the Transportation R

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“…( 2015 ) Event Detection Twitter X Huizinga et al. ( 2017 ) Event Detection Twitter X X Acosta and Palaoag ( 2019 ) Event Detection Twitter X Rodavia et al. ( 2018 ) Event Detection Twitter X Jung et al.…”

Section: Fields Of Applicationmentioning

confidence: 99%

A survey on the use of association rules mining techniques in textual social media

2022

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The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.

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A methodology for the resolution of cashtag collisions on Twitter – A natural language processing & data fusion approach

Evans

Owda

Crockett

et al. 2019

Expert Systems with Applications

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Exploring Insurance and Natural Disaster Tweets Using Text Analytics

Cited by 3 publications

References 24 publications

Impacts of Precautionary and Opportunistic Buying Behaviors and Supply Issues on Supply Chain Resilience During the COVID-19 Pandemic

Impacts of Precautionary and Opportunistic Buying Behaviors and Supply Issues on Supply Chain Resilience During the COVID-19 Pandemic

A survey on the use of association rules mining techniques in textual social media

A methodology for the resolution of cashtag collisions on Twitter – A natural language processing & data fusion approach

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