Cirqueira, Douglas and Almeida, Fernando and Cakir, Gültekin and Jacob, Antonio and Lobato, Fabio and Bezbradica, Marija and Helfert, Markus (2020) Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail. In: WUDESHI-DR 2020, 5-6 November 2020, Virtual.

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Abstract

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)
Additional Information:	This research was supported by the European Union Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 765395; and supported, in part, by Science Foundation Ireland grant 13/RC/2094.
Keywords:	Sentiment Analysis; Explainable Artificial Intelligence; Digital Retail; Crisis Management;
Academic Unit:	Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	14089
Depositing User:	Gultekin Cakir
Date Deposited:	26 Feb 2021 16:25
Refereed:	Yes
Funders:	Horizon 2020, Grant Agreement No. 765395
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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