Open Access

ARTICLE

AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments

Fahim K. Sufi^1,*, Musleh Alsulami²

1 Federal Government, Melbourne, VIC, 3000, Australia
2 Information Systems Department, Umm Al-Qura University (UQU), Makkah, 24382, Saudi Arabia

* Corresponding Author: Fahim K. Sufi. Email:

Computers, Materials & Continua 2022, 72(2), 3631-3649. https://doi.org/10.32604/cmc.2022.026272

Received 20 December 2021; Accepted 22 February 2022; Issue published 29 March 2022

Abstract

The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis. We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022. The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021. During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities. In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively. As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition. According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.

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