Journal
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
Volume 33, Issue 7, Pages 890-898Publisher
ELSEVIER
DOI: 10.1016/j.jksuci.2019.04.011
Keywords
Sentiment analysis; Opinion extraction; Arabic language; Lexicon; Semantic segmentation; Supervised approach
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This paper proposes a new approach to analyze sentiments for the Arabic language by building new lexical resources and integrating morphological notions. The resources are used to construct a supervised model and semantically segment the lexicon to improve execution time.
In this paper we propose a new approach to analyze sentiments for the Arabic language. To overcome the scarcity and size limitation of the required Arabic language resources for training and analysis tasks, we built new lexical resources using different approaches. We have also integrated the morphological notion by creating both stemmed and lemmatized versions of word lexicons. Thereafter, the generated resources were used in the construction of a supervised model from a set of features considering the word negation context. Similarly, we have semantically segmented the lexicon in order to reduce the model vectors size and consequently improve the execution time. CO 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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