Text Classification of CrowdSourced Data Using Parallel Computing Nested Baye’s Classifier

Abstract: Text classification has drawn attention due to the vast applicability in language identification, spam filtering, genre classification, sentiment analysis, readability assessment, article triage etc. With the goal of classifying crowdsourced data which is gathered from many websites into text classification, Crowdsourced data classification has challenges in getting data from various sources and whatever the data we are getting is huge in amount. Our approach will address challenges to classify the crowdsourced data in parallel computing in which many calculations are carried out simultaneously so that the crowdsourced data will be divided into tree format and calculations will be done simultaneously using nested baye�s classifier. Our aim is to classify the algorithms which are obtained using web crawler and will be converted into programmes using nested baye�s classifier technique.

Keywords: Web Crawler, Text Classification, Nested Bayes Classifier, Parallel Computing, CrowdSourcing.