1Cademy - COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

There has been mass fear and panic phenomena due to incomplete and often inaccurate information.
There is a need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented.
In the article, "COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification" by Jim Samuel research article, public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages are identified.
There have been insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States. The article uses descriptive textual analytics supported by necessary textual data visualizations.
Furthermore, the article shows a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compares their effectiveness in classifying Coronavirus Tweets of varying lengths.
Samuel observes a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method.
There is also an observation made that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets.
The research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Updated 2020-07-25

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