Analysis of Text Classification of Dataset Using NB-Classifier

International Journal of Computer Science and Engineering
© 2020 by SSRG - IJCSE Journal
Volume 7 Issue 6
Year of Publication : 2020
Authors : Asif Ansari , Sreenarayanan NM

How to Cite?

Asif Ansari , Sreenarayanan NM, "Analysis of Text Classification of Dataset Using NB-Classifier," SSRG International Journal of Computer Science and Engineering , vol. 7,  no. 6, pp. 24-28, 2020. Crossref,


This is period of Modernization, when we need to purchase something we generally think about the authentic and prominence of the item. Be that as it may, the notoriety of the item relies on the audit which is given by the individuals. For instance once film discharged, we check the notoriety of the film through the audits, remarks and rating which is given by various individuals. These audits assume a significant job for the film and the business and furthermore for exposure.
In this bustling life individuals don't have part of time to experience every single surveys. Also, now nostalgic investigation assumes a significant job. This is fundamentally the way toward arranging the estimations or assessments of the individuals. In notion investigation there are just two choices constructive and contrary which makes individuals to like or abhorrence the film or anything, Individuals will as of now post their surveys of item
at business destinations about the items. Presently in the event that anybody needs to purchase an item, one is essentially simply experience the audits,
appraisals and one settle on the choice effectively .In this paper Naives Bayes classifier use to play out the nostalgic examination. To play out the procedure AI calculation use to reach to the methodologies. For that Highlights determination improves the presentation of estimation order, so we need to choose increasingly more element for exact grouping. So Naives Bayes characterization performs preferred and quick over other grouping


text classification; NB Classification Algorithm; Feature Extraction; Classification of Text.


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