A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis

Main Article Content

Ahmed Sabah AL-Jumaili

Abstract

          Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discriminate the polarity of sentiments. This paper proposes a hybrid method of linguistic and statistical features along with classification methods for Arabic sentiment analysis. Linguistic features contains stemming and POS tagging, while statistical contains the TF-IDF. A benchmark dataset of Arabic tweets have been used in the experiments. In addition, three classifiers have been utilized including SVM, KNN and ME. Results showed that SVM has outperformed the other classifiers by obtaining an f-score of 72.15%. This indicates the usefulness of using SVM with the proposed hybrid features.

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A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis. Baghdad Sci.J [Internet]. 2020 Mar. 18 [cited 2024 Dec. 28];17(1(Suppl.):0385. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/2977
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How to Cite

1.
A Hybrid Method of Linguistic and Statistical Features for Arabic Sentiment Analysis. Baghdad Sci.J [Internet]. 2020 Mar. 18 [cited 2024 Dec. 28];17(1(Suppl.):0385. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/2977
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Mohammed Hadwan, Mohammed A. Al-Hagery, Mohammed Al-Sarem, Faisal Saeed (2022)
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Advancements and challenges in Arabic sentiment analysis: A decade of methodologies, applications, and resource development. Heliyon, 10(21), e39786.
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Maheswari M. (2024-01-01)
Comprehensive Analysis of Arabic Sentiment Analysis using Lexicon and Machine Learning based Approaches. 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024.
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Noureen (2024-01-01)
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Kang D.W. (2024-01-01)
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Saha S. (2023-01-01)
An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach. Baghdad Science Journal, 20, 1164-1181.
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Hassoon I.M. (2023-01-01)
CFNN for Identifying Poisonous Plants. Baghdad Science Journal, 20(3 (Suppl.)), 1122-1130.
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Tayyeh H.K. (2022-02-01)
A combination of least significant bit and deflate compression for image steganography. International Journal of Electrical and Computer Engineering, 12(1), 358-364.
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Alqaan S.E. (2022-01-01)
Utilizing Sentiment Analysis to Enhance the Quality of Online Learning. Proceedings of 2022 5th National Conference of Saudi Computers Colleges, NCCC 2022, 41-46.
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Hameed N.H. (2022-01-01)
Short Text Semantic Similarity Measurement Approach Based on Semantic Network. Baghdad Science Journal, 19(6), 1581-1591.
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Saudy R.E. (2022-01-01)
A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews. International Journal of Advanced Computer Science and Applications, 13(8), 423-432.
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Alharbi B.A. (2022-01-01)
Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia. International Journal of Advanced Computer Science and Applications, 13(6), 717-726.
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Al-Jumaili A.S.A. (2022-01-01)
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Rabani S.T. (2020-12-01)
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Elmitwally N.S. (2020-10-01)
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