Appraisal of Two Arabic Opinion Summarization Methods: Statistical Versus Machine Learning. (16th April 2020)

• Record Type: Journal Article
• Authors: Touati, Imen; Ellouze, Mariem; Graja, Marwa; Hadrich Belguith, Lamia
• Is Part Of: Computer journal. Volume 65:Number 2(2022)
• Page Start: 192
• Page End: 202
• Publication Date: 2020-04-16
• Subjects: opinion analysis -- Arabic opinion summarization -- maximum entropie -- multi-criteria analysis -- opinion-specific feature -- textual feature; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa007
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: In this paper, we propose to overcome the challenge of digesting opinions in a news article. Our objective is to provide a summary of opinions delivered by many sources about a main topic in an Arabic news article. In literature, several studies addressed issues related to opinion summarization. However, we noticed a lack of studies that address this problem in Arabic language. So, we have proposed two different methods: multi-criteria and machine learning-based methods. We proceed by comparing the results provided by the proposed methods for opinionated sentence extraction. The proposed methods were evaluated using two feature types: text-based features and opinion-specific features. Experimental results show the robustness of machine learning method to extract opinionated sentences with consideration of two sets of features.