A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

Swarn Avinash Kumar; Moustafa M Nasralla; Iván García-Magariño; Harsh Kumar

doi:10.7717/peerj-cs.713

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

PeerJ Comput Sci. 2021 Sep 17:7:e713. doi: 10.7717/peerj-cs.713. eCollection 2021.

Authors

Swarn Avinash Kumar¹, Moustafa M Nasralla², Iván García-Magariño^{3

4}, Harsh Kumar⁵

Affiliations

¹ IIIT Allahabad, Uttar Pradesh, India.
² Department of Communications and Networks Engineering, Prince Sultan University, Riyadh, Saudi Arabia.
³ Universidad Complutense de Madrid, Madrid, Spain.
⁴ Instituto de Tecnología del Conocimiento, UCM, Madrid, Spain.
⁵ Peoples' Friendship University of Russia, Moscow, Russia.

Abstract

The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

Keywords: Business intelligence; COVID-19; Decision support system; Machine learning; Pandemics; Sentiment analysis.

Grants and funding

Prince Sultan University provided the Article Processing Charges for this publication. This work is in the context of the project “CITIES: Ciudades inteligentes totalmente integrales, eficientes y sotenibles”' (ref. 518RT0558) funded by CYTED (“Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo”) and “Diseño colaborativo para la promoción del bienestar en ciudades inteligentes inclusivas” (TIN2017-88327-R) funded by the Spanish council of Science, Innovation and Universities from the Spanish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.