Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail

Douglas Cirqueira, Fernando Almeida, Gültekin Cakir, Antonio Jacob, Fabio Lobato, Fabio Lobato, Marija Bezbradica, Markus Helfert

2020

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.

Paper Citation

in Harvard Style

Cirqueira D., Almeida F., Cakir G., Jacob A., Lobato F., Lobato F., Bezbradica M. and Helfert M. (2020). Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail.In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications - Volume 1: WUDESHI-DR, ISBN 978-989-758-480-0, pages 319-328. DOI: 10.5220/0010215303190328

in Bibtex Style

@conference{wudeshi-dr20,
author={Douglas Cirqueira and Fernando Almeida and Gültekin Cakir and Antonio Jacob and Fabio Lobato and Fabio Lobato and Marija Bezbradica and Markus Helfert},
title={Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail},
booktitle={Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications - Volume 1: WUDESHI-DR,},
year={2020},
pages={319-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010215303190328},
isbn={978-989-758-480-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications - Volume 1: WUDESHI-DR,
TI - Explainable Sentiment Analysis Application for Social Media Crisis Management in Retail
SN - 978-989-758-480-0
AU - Cirqueira D.
AU - Almeida F.
AU - Cakir G.
AU - Jacob A.
AU - Lobato F.
AU - Lobato F.
AU - Bezbradica M.
AU - Helfert M.
PY - 2020
SP - 319
EP - 328
DO - 10.5220/0010215303190328