A hybrid method of linguistic and statistical features for Arabic sentiment analysis
Other Title(s)
دمج الآليات اللغوية و الإحصائية لتحليل الرأي في اللغة العربية
Joint Authors
al-Jumayli, Ahmad Sabah Ahmad
Tayih, Huda Kazim
Source
Issue
Vol. 17, Issue 1 (sup) (31 Mar. 2020), pp.385-390, 6 p.
Publisher
University of Baghdad College of Science for Women
Publication Date
2020-03-31
Country of Publication
Iraq
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
al-Jumayli, Ahmad Sabah Ahmad& Tayih, Huda Kazim. 2020. A hybrid method of linguistic and statistical features for Arabic sentiment analysis. Baghdad Science Journal،Vol. 17, no. 1 (sup), pp.385-390.
https://search.emarefa.net/detail/BIM-970047
Modern Language Association (MLA)
al-Jumayli, Ahmad Sabah Ahmad& Tayih, Huda Kazim. A hybrid method of linguistic and statistical features for Arabic sentiment analysis. Baghdad Science Journal Vol. 17, no. 1 (Supplement) (Mar. 2020), pp.385-390.
https://search.emarefa.net/detail/BIM-970047
American Medical Association (AMA)
al-Jumayli, Ahmad Sabah Ahmad& Tayih, Huda Kazim. A hybrid method of linguistic and statistical features for Arabic sentiment analysis. Baghdad Science Journal. 2020. Vol. 17, no. 1 (sup), pp.385-390.
https://search.emarefa.net/detail/BIM-970047
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 389-390
Record ID
BIM-970047