Abstract: Most of opinion mining works need lexical resources for opinion which recognize the polarity of words (positive/ negative) regardless their contexts which called prior polarity. The word prior polarity may be changed when it is considered in its contexts, for example, positive words may be used in phrases expressing negative sentiments, or vice versa. In this paper, we aim at generating sentiment Arabic lexical semantic database having the word prior coupled with its contextual polarities and the related phrases. To do that, we study first the prior polarity effects of each word using our Sentiment Arabic Lexical Semantic Database on the sentence-level subjectivity and Support Vector Machine classifier. We then use the seminal English two-step contextual polarity phrase-level recognition approach to enhance word polarities within its contexts. Our results achieve significant improvement over baselines.

Samir E. Abdelrahman, Hanaa Mobarz, Ibrahim Farag and Mohsen Rashwan, “Arabic Phrase-Level Contextual Polarity Recognition to Enhance Sentiment Arabic Lexical Semantic Database Generation” International Journal of Advanced Computer Science and Applications(IJACSA), 5(10), 2014. http://dx.doi.org/10.14569/IJACSA.2014.051005

@article{Abdelrahman2014,
title = {Arabic Phrase-Level Contextual Polarity Recognition to Enhance Sentiment Arabic Lexical Semantic Database Generation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2014.051005},
url = {http://dx.doi.org/10.14569/IJACSA.2014.051005},
year = {2014},
publisher = {The Science and Information Organization},
volume = {5},
number = {10},
author = {Samir E. Abdelrahman and Hanaa Mobarz and Ibrahim Farag and Mohsen Rashwan}
}