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
fDate :
4/1/2015 12:00:00 AM
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"
Conference_Titel :
Arabic Computational Linguistics (ACLing), 2015 First International Conference on
Print_ISBN :
978-1-4673-9154-2
DOI :
10.1109/ACLing.2015.22