Introducing The Semantics In Sentiment Analysis On Twitter Using WordNet

Please use this identifier to cite or link to this item: https://dspace.univ-ouargla.dz/jspui/handle/123456789/7833

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DC Field	Value	Language
dc.contributor.author	Ines Mostefai, Zakaria Elberrichi	-
dc.date.accessioned	2014-11-18T14:18:17Z	-
dc.date.available	2014-11-18T14:18:17Z	-
dc.date.issued	2014-11-18	-
dc.identifier.issn	dmz	-
dc.identifier.uri	http://dspace.univ-ouargla.dz/jspui/handle/123456789/7833	-
dc.description	2émes journées internationales de chimie organométallique et catalyse jicoc’2014	en_US
dc.description.abstract	Social networks are an excellent source of information, and opinion extraction. The present work shows the introducing of the semantics for sentiment analysis on Twitter using the Machine Learning Approach and WordNet lexical database. The best performance was obtained using the SVM classifier for the machine learning approach with a very good F- measure of 90.75%.	en_US
dc.language.iso	en	en_US
dc.relation.ispartofseries	2014;	-
dc.subject	Sentiment analysis	en_US
dc.subject	Twitter	en_US
dc.subject	Machine Learning	en_US
dc.subject	WordNet	en_US
dc.subject	SVM	en_US
dc.subject	Naïve Bayes	en_US
dc.title	Introducing The Semantics In Sentiment Analysis On Twitter Using WordNet	en_US
dc.type	Article	en_US
Appears in Collections:	1. Faculté des mathématiques et des sciences de la matière

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