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A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect

Guellil, Imane; Adeel, Ahsan; Azouaou, Faical; Benali, Fodil; Hachani, Ala-Eddine; Dashtipour, Kia; Gogate, Mandar; Ieracitano, Cosimo; Kashani, Reza; Hussain, Amir

Authors

Imane Guellil

Ahsan Adeel

Faical Azouaou

Fodil Benali

Ala-Eddine Hachani

Cosimo Ieracitano

Reza Kashani



Abstract

In this paper, we propose a semi-supervised approach for sentiment analysis of Arabic and its dialects. This approach is based on a sentiment corpus, constructed automatically and reviewed manually by Algerian dialect native speakers. This approach consists of constructing and applying a set of deep learning algorithms to classify the sentiment of Arabic messages as positive or negative. It was applied on Facebook messages written in Modern Standard Arabic (MSA) as well as in Algerian dialect (DALG, which is a low resourced-dialect, spoken by more than 40 million people) with both scripts Arabic and Arabizi. To handle Arabizi, we consider both options: transliteration (largely used in the research literature for handling Arabizi) and translation (never used in the research literature for handling Arabizi). For highlighting the effectiveness of a semi-supervised approach, we carried out different experiments using both corpora for the training (i.e. the corpus constructed automatically and the one that was reviewed manually). The experiments were done on many test corpora dedicated to MSA/DALG, which were proposed and evaluated in the research literature. Both classifiers are used, shallow and deep learning classifiers such as Random Forest (RF), Logistic Regression(LR) Convolutional Neural Network (CNN) and Long short-term memory (LSTM). These classifiers are combined with word embedding models such as Word2vec and fastText that were used for sentiment classification. Experimental results (F1 score up to 95% for intrinsic experiments and up to 89% for extrinsic experiments) showed that the proposed system outperforms the existing state-of-the-art methodologies (the best improvement is up to 25%).

Journal Article Type Article
Acceptance Date Nov 27, 2020
Online Publication Date Feb 27, 2021
Publication Date 2021
Deposit Date Apr 27, 2022
Publicly Available Date Apr 27, 2022
Journal SN Computer Science
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 2
Article Number 118
DOI https://doi.org/10.1007/s42979-021-00510-1
Keywords Arabizi, Sentiment analysis, Arabic, Arabic dialect, Translation, Transliteration
Public URL http://researchrepository.napier.ac.uk/Output/2866999

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