Open AccessPreparing Annotated Data on Covid -19 by Employing Naïve Bayes
Author(s) -
Dipankar Das,
Adarsh Ghosh,
Adityar Rayala,
Dibyajyoti Dhar,
Vidit Sarkar,
Avishek Garain,
Sourav Kumar
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.111211
Subject(s) - naive bayes classifier , computer science , hash function , covid-19 , event (particle physics) , bayes' theorem , core (optical fiber) , pandemic , artificial intelligence , computer security , bayesian probability , telecommunications , medicine , physics , disease , pathology , quantum mechanics , support vector machine , infectious disease (medical specialty)
The on-going pandemic has opened the pandora’s box of the plethora of hidden problems which the society has been hiding for years. But the positive side to the present scenario is the opening up of opportunities to solve these problems on the global stage. One such area which was being flooded with all kinds of different emotions, and reaction from the people all over the world, is twitter, which is a micro blogging platform. Coronavirus related hash tags have been trending all over for many days unlikeany other event in the past. Our experiment mainly deals with the collection, tagging and classification of these tweets based on the different keywords that they may belong to, using the Naive Bayes algorithm atthe core.