Farahat, F., Hamouda, A., Rashid, A. (2016). Sentiment Analysis System for Arabic Articles News (SASAAN). The Egyptian Journal of Language Engineering, 3(2), 14-24. doi: 10.21608/ejle.2016.60181
Fawzia Zaki Farahat; Alaa Hamouda; Ali Mahmoud Rashid. "Sentiment Analysis System for Arabic Articles News (SASAAN)". The Egyptian Journal of Language Engineering, 3, 2, 2016, 14-24. doi: 10.21608/ejle.2016.60181
Farahat, F., Hamouda, A., Rashid, A. (2016). 'Sentiment Analysis System for Arabic Articles News (SASAAN)', The Egyptian Journal of Language Engineering, 3(2), pp. 14-24. doi: 10.21608/ejle.2016.60181
Farahat, F., Hamouda, A., Rashid, A. Sentiment Analysis System for Arabic Articles News (SASAAN). The Egyptian Journal of Language Engineering, 2016; 3(2): 14-24. doi: 10.21608/ejle.2016.60181
Sentiment Analysis System for Arabic Articles News (SASAAN)
2Faculty of Engineering, Al-Azhar University, Department of Systems and Computers Engineering
3Systems & Computer Department, Faculty of Engineering.
Abstract
Sentiment analysis (also known as opinion mining) identifies and analyzes opinions and emotions in many domains (e.g. news, articles, product reviews, blogs, forum posts). Opinion mining is very important for companies, governments and every one interested to know opinion about special subject. This research discusses the problem of identifying opinion in Arabic news and Arabic articles. Most previous researches focused on extracting opinion from direct sentiments at the level of the article. Considering that an article contains large number of sentences, and some of these sentences may be about different topics and may be not opinion sentence, we propose a new methodology for sentiment analysis for Arabic articles. It starts with identifying opinion sentence related to the target of the article. Machine learning and Typed Dependency Relations (TDR) are used to identify the opinion sentences. Sentences that contain one word of high frequency nouns or adjectives are classified as target sentences. Then opinion lexicon is built using machine learning based on dataset that was collected from different domains (e.g. politics, economy, government, sports, and art). Three methods are used to identify opinion mining in articles. A method that depends on Opinion Lexicon achieved F-score of 62.8%. Machine learning (SVM) method achieved F-score 42.63%. whereas, our method that identifies opinion sentences that are related to the target of article then using opinion lexicon achieved the best results (F-score of 73.25%). So we recommended to identify opinion sentences that are related to the target of the article, then use the opinion lexicon to know the opinion.
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