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Explainable sentiment analysis application for social media crisis management in retail

Cirqueira, Douglas orcid logoORCID: 0000-0002-1283-0453, Almeida, Fernando, Cakir, Gültekin orcid logoORCID: 0000-0001-9715-7167, Jacob, Antonio orcid logoORCID: 0000-0002-9415-7265, Lobato, Fabio orcid logoORCID: 0000-0002-6282-0368, Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 and Helfert, Markus orcid logoORCID: 0000-0001-6546-6408 (2020) Explainable sentiment analysis application for social media crisis management in retail. In: 4th International Conference on Computer-Human Interaction Research and Applications - Volume 1: WUDESHI-DR, 5-6 Nov 2020, Budapest, Hungry (Online). ISBN 978-989-758-480-0

Sentiment Analysis techniques enable the automatic extraction of sentiment in social media data, including popular platforms as Twitter. For retailers and marketing analysts, such methods can support the understanding of customers' attitudes towards brands, especially to handle crises that cause behavioural changes in customers, including the COVID-19 pandemic. However, with the increasing adoption of black-box machine learning-based techniques, transparency becomes a need for those stakeholders to understand why a given sentiment is predicted, which is rarely explored for retailers facing social media crises. This study develops an Explainable Sentiment Analysis (XSA) application for Twitter data, and proposes research propositions focused on evaluating such application in a hypothetical crisis management scenario. Particularly, we evaluate, through discussions and a simulated user experiment, the XSA support for understanding customer's needs, as well as if marketing analysts would trust such an application for their decision-making processes. Results illustrate the XSA application can be effective in providing the most important words addressing customers sentiment out of individual tweets, as well as the potential to foster analysts' confidence in such support.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Sentiment Analysis; Explainable Artificial Intelligence; Digital Retail; Crisis Management
Subjects:Business > Marketing
Business > Consumer behaviour
Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > Lero: The Irish Software Engineering Research Centre
Published in: Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2020). 1. ScitePress. ISBN 978-989-758-480-0
Official URL:http://dx.doi.org/10.5220/0010215303190328
Copyright Information:© 2020 ScitePress. CC-BY-NC-ND 4.0
Funders:European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 765395, Science Foundation Ireland grant 13/RC/2094
ID Code:25196
Deposited On:23 Nov 2020 14:38 by Douglas Da Rocha cirqueira . Last Modified 28 Mar 2022 12:21

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