Abstract: In this age, the Internet has empowered the flow of thoughts and data and has thus expanded the knowledge base among individuals. Be that as it may, this has its own disadvantages in terms of false and fake data. The need to uncover such bogus data and disdain discourse during this COVID-19 pandemic has never been more important. Data mining is fundamentally used to identify relevant required data available on the internet or any data source. Consolidating the information mining using different methods like content mining, NLP and computational insight, we can arrange Corona virus tweets as great, awful or unbiased. The objective of this work is on the characterization of feelings of ‘Corona virus tweets' information assembled from Twitter. Moreover, we can characterize the Covid-19 Texts as phony or not. To improve characterization we are utilizing AI methods for improving the effectiveness and quality of the proposed approach.
Keywords: NLP, Naïve Bayes, Covid-19 data, Fake news detection, Sentiment Classification
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DOI:
10.17148/IJARCCE.2021.10556
