DocumentCode
3756111
Title
Increasing the Accuracy of Opinion Mining in Arabic
Author
Sasi Atia;Khaled Shaalan
Author_Institution
IT Dept., Dubai Stat. center, Dubai, United Arab Emirates
fYear
2015
fDate
4/1/2015 12:00:00 AM
Firstpage
106
Lastpage
113
Abstract
Opinion Mining is a raising research field of interest, with its different applications derived by market needs to analyze product reviews or to assess the public opinion, for political reasons, during presidential campaigns. In this paper, we address an approach for improving accuracy of Opinion Mining in Arabic. In order to conduct our study we need Arabic linguistic resources for opinion mining. Investigating the available resources we found that the OCA corpus is available and sufficient to prove our approach. Experimental results showed that applying different parameters of the machine learning classifiers on the OCA corpus leads to increasing the accuracy of the Arabic Opinion Mining.
Keywords
"Predictive models","Data mining","Support vector machines","Niobium","Sentiment analysis","Text processing","Automobiles"
Publisher
ieee
Conference_Titel
Arabic Computational Linguistics (ACLing), 2015 First International Conference on
Print_ISBN
978-1-4673-9154-2
Type
conf
DOI
10.1109/ACLing.2015.22
Filename
7422287
Link To Document