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

Title:	Introducing The Semantics In Sentiment Analysis On Twitter Using WordNet
Authors:	Ines Mostefai, Zakaria Elberrichi
Keywords:	Sentiment analysis Twitter Machine Learning WordNet SVM Naïve Bayes
Issue Date:	18-Nov-2014
Series/Report no.:	2014;
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%.
Description:	2émes journées internationales de chimie organométallique et catalyse jicoc’2014
URI:	http://dspace.univ-ouargla.dz/jspui/handle/123456789/7833
ISSN:	dmz
Appears in Collections:	1. Faculté des mathématiques et des sciences de la matière

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