Abstract:
Online media applications are the most popular and convenient method to get
the news across the globe. There were some instances of unethical behavior by
the media when reporting misleading and fake news. This research presents a
method to perform a sentiment analysis specifically designed to work with
COVID-19 social media news, taking into account their structure, length, and
specific keywords related to the language used. Initially, the data were revealed
through pre-processing of media news posts to normalize the language, by
cleaning text data and generalizing the text to express the sentiment. The text
data collection from COVID-19 social media posts and news websites was
investigated in the data processing stage. This paper proposes the use of a text
blob library as an approach for spot modification in the polarity of the
sentiment expressed. Adopting a sentiment analysis through the social audience
about the COVID-19 pandemic, their emotions, and moods are split by words
to reveal how people feel exactly about the impact of COVID-19 media news.
This research contributes to building a training model to identify the sentiment
and it will enhance the sentiment classification performance, irrespective of the
domain and distribution of the test set.
