LANGUAGE ATTITUDE AND SENTIMENT ANALYSES IN GETTING THE INSIGHTS TOWARDS COVID-19’S OMICRON VARIANT NEWS

Omicron variant has been massively reported on Indonesian mass media following the spread of other previous variants during Covid-19 pandemic. This research combines computer science and linguistics to analyze news variant. It implemented quantitative using computational algorithm by collecting titles from mainstream online media. Sentiment Analysis (SA) was applied obtain sentiments, opinion, subjectivities texts along with topic modeling in classifying topics. The words headline were used as data grabbed Python programming language. A criterion-based sampling employed select relevant formulate criteria methodology. results filtered ‘Omicron’ keyword for SA processing Azure Text tool. SA, research, then confirmed Attitude (AA) perspective Systemic Functional Linguistics. AA classified into affect, judgment, appreciation attitude construed English text. provides insights issue. presence extracts bipolar senses human’s meaning interpretation. is important straighten findings. contextual problem requires study its ‘neutral’ which are confidently directed positive or negative meanings AA. found that there different dynamics findings they reflect particular meanings. Besides their difference, useful managing overload fast policy making whereas makes sure acceptable people. In this case corrects bias occurring SA.