AI-based Automated Extraction of Entities, Entity Categories and Sentiments on COVID-19 Situation

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. For example, many international travelers who are unable to return to their residential locations owing to measures such as lockdowns, curfews, and the suspension of flights are posting their dissatisfaction over government efficiency in dealing with this situation. Therefore, understanding location-oriented sentiments about this situation is of prime importance for diplomats, 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 and tested this algorithm on live Twitter feeds from July 15, 2021 to August 10, 2021. Out of 1866 tweet messages analyzed, 990 Tweets contained one or more location entities. In total 1,322 location entities were detected falling under city, contitinent, country region, language and state entity categories.