A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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%).
Original languageEnglish
Article number118
JournalSN Computer Science
Volume2
Issue number2
Early online date27 Feb 2021
DOIs
Publication statusE-pub ahead of print - 27 Feb 2021

Bibliographical note

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Keywords

  • Original Research
  • Social Media Analytics and its Evaluation
  • Arabizi
  • Sentiment analysis
  • Arabic
  • Arabic dialect
  • Translation
  • Transliteration

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