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Political Arabic Articles Orientation Using Rough Set Theory with Sentiment Lexicon

Alwan, JK, Hussain, A, Abd, DH, Sadiq, AT, Khalaf, M and Liatsis, P (2021) Political Arabic Articles Orientation Using Rough Set Theory with Sentiment Lexicon. IEEE Access. ISSN 2169-3536

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Abstract

Sentiment analysis is an emerging research field that can be integrated with other domains, including data mining, natural language processing and machine learning. In political articles, it is difficult to understand and summarise the state or overall views due to the diversity and size of social media information. A number of studies were conducted in the area of sentiment analysis, especially using English texts, while Arabic language received less attention in the literature. In this study, we propose a detection model for political orientation articles in the Arabic language. We introduce the key assumptions of the model, present and discuss the obtained results, and highlight the issues that still need to be explored to further our understanding of subjective sentences. The main purpose of applying this new approach based on Rough Set (RS) theory is to increase the accuracy of the models in recognizing the orientation of the articles. We present extensive simulation results, which demonstrate the superiority of the proposed model over other algorithms. It is shown that the performance of the proposed approach significantly improves by adding discriminating features. To summarize, the proposed approach demonstrates an accuracy of 85.483%, when evaluating the orientation of political Arabic datasets, compared to 72.58% and 64.516% for the Support Vector Machines and Naïve Bayes methods, respectively.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers
Date Deposited: 28 Jan 2021 09:24
Last Modified: 28 Jan 2021 10:00
DOI or Identification number: 10.1109/ACCESS.2021.3054919
URI: https://researchonline.ljmu.ac.uk/id/eprint/14338

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