An analysis of Yemenis' responses and sentiments on social media towards the emergence of the COVID-19 pandemic)‎

Abstract EN

Recently, many studies have widely dealt with data mining and Text classification, including sentiment analysis.

Sentiment analysis (SA) is an application of Natural Language Processing (NLP) implemented to understand the public's attitudes.

The recent proliferation of social media has helped gauge the public's mood.

The current study aims to explore the influence of the COVID-19 pandemic on the Yemeni community and generate indices assessing public sentiments and attitudes using lexicon and rule-based approach (VAEDR : Valence Aware Dictionary and Sentiment Reasoner) and qualitative and quantitative analysis methods.

8,830 Facebook and YouTube comments were analyzed before and after the declaration of COVID-19 on 10th April 2020 in Yemen.

The results revealed that sentiment polarity with and without contextual reference differed significantly.

Without contextual reference, neutrality was prevalent and reached 55%; negativity scored 24% while positivity reached 21% before 10th April, but after this date, negativity was dominant and reached 57%, neutrality scored 28%, and positivity scored 15%.

With contextual reference, positivity was prevalent and scored 72% before 10th April, but after this date, negativity dominated the public's mood and reached 78.23%; positivity highly decreased to 18.65%, while neutrality scored 3.12%.

The study demonstrated the superiority of SA based on the contextual reference of words.