Volume 12, Number 2, December 2016 - DOI: http://dx.doi.org/10.21700/ijcis.2016.274

IJCIS

Computing and Information Sciences is a peer reviewed journal that is committed to timely publication of original research, surveying and tutorial contributions on the analysis and development of computing and information science. The journal is designed mainly to serve researchers and developers, dealing with information and computing. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. The journal is published 2-3 times per year with distribution to librarians, universities, research centers, researchers in computing, mathematics, and information science. The journal maintains strict refereeing procedures through its editorial policies in order to publish papers of only the highest quality. The refereeing is done by anonymous Reviewers. Often, reviews take four months to six months to obtain, occasionally longer, and it takes an additional several months for the publication process.

DOI: http://dx.doi.org/10.21700/ijcis.2016.118

Sentiment Analysis of Arabic Tweets Using Semantic Resources

Lamia Al-Horaib* email: lamo4e@hotmail.com
Muhammad Badruddin Khan

Department of Information Systems, Al Imam Mohammad Ibn Saud Islamic University, Saudia Arabia

*Corresponding author

Received: 9 June 2016
Revised: 10 June 2016
Accepted: 21 August 2016
Published: 25 December 2016

Abstract: Sentiment analysis has grown to be one of the most active research areas in natural language processing and text mining. Many researchers have investigated sentiment analysis and opinion mining from different classification approaches. However, limited research is conducted on Arabic sentiment analysis as compared to the English language. In this paper, we have proposed and implemented a technique for Twitter Arabic sentiment analysis consisting of a semantic approach and Arabic linguistic features. Hence, we introduced a mechanism for preprocessing Arabic tweets, and for the methodology of sentiment classification we used a semantic approach. Also, we proposed a technique of classification which uses both Arabic and English sentiment lexicons to classify the Arabic tweets into three sentiment categories (positive or negative or neutral). Our experiments show that many issues were encountered when we used the Arabic SentiWordNet facility to classify Arabic tweets directly; these issues are basically related to Arabic text processing. The Arabic lexicons and Arabic tools must be improved or built from scratch in order to improve Arabic sentiment analysis using the semantic approach. The improvement in results, which are due to our contribution in the form of enhanced Arabic lexicons and amended Arabic tools, demonstrate this need.

Keywords: Arabic Lexicons; Sentiment Analysis; Semantic Approach; SentiWordNet; Twitter.


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