Componential Analysis of Covid-19 Related Terms on Google Scholar Articles

Abstract

Componential analysis of meaning is based on the hypothesis that each lexical unit is composed of a certain number of semantic components and that the words of a language can be grouped into semantic domains. Semantic features enable us to explain how words that share certain features may be members of the same semantic domain. The objective of this study is decomposing the semantic features of Covid-19 related terms, categorizing them into semantic domains and lastly to see how the terms are used in their original articles according to the meaning properties. The basis of meaning properties include being meaningful or meaningless – they are deemed meaningless if the words in used contains ambiguity, redundancy, anomaly, and contradictory. In this study, qualitative method was used; six different articles related to Covid-19 were selected on Google Scholar as the source of data. From six selected articles, thirty eight recurring terms were found related to covid-19. The thirty eight Covid-19 terms then are broken down into their semantic features. Then, they are put into six semantic domains according to their shared semantic features. Lastly, the application of these thirty eight terms is analyzed by its meaning properties. The result shows there are nine phrases or sentences that are anomaly, five redundancies and two ambiguities in the original articles. The findings show that even in Google Scholar articles, some of the Covid-19 terms are not used meaningfully in the sentences. The conclusion of the study is that the semantic features of each Covid-19 related terms can indeed give clear descriptions of the total meaning of each word. Besides that, the semantic features served to find the similar terms in terms of meaning to be grouped into one semantic domain, at the same time also distinguish the meaning of one term to another in that one same semantic domain.