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An Overview of Word Embedding Models Evaluation for Arabic Sentiment Analysis

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Proceedings of the 5th International Conference on Big Data and Internet of Things (BDIoT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

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Abstract

Sentiment Analysis (SA) or opinion mining has become one of the essential research fields whose application is clearly visible in a large variety of domains. Due to Arabic structure complexities at the level of morphology, orthography, and dialects, manual feature extraction is a time-consuming and challenging mission, Besides, Arabic Sentiment analysis (ASA) is very difficult and is considered a more difficult task compared to other languages.

In this paper, we discuss the issue of word embedding models that is very important in the field of Arabic Sentiment Analysis also provide a comparative analysis of the most famous word embedding models namely: Word2Vec, Glove, FastText, Elmo, and Bert. We found through this deep evaluation, that FaxtText, Elmo, and Bert embedding models outperform other Models.

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Author information

Authors and Affiliations

  1. Information System and Software Engineering Laboratory, Abdelmalek Essaadi University, Tetuan, Morocco

    Youssra Zahidi & Yacine El Younoussi

  2. Information Technology and Modeling Systems (TIMS) Research Team, Abdelmalek Essaadi University, Tetuan, Morocco

    Yassine Al-Amrani

Authors
  1. Youssra Zahidi
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  2. Yacine El Younoussi
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  3. Yassine Al-Amrani
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Corresponding author

Correspondence to Youssra Zahidi .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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Zahidi, Y., El Younoussi, Y., Al-Amrani, Y. (2022). An Overview of Word Embedding Models Evaluation for Arabic Sentiment Analysis. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_31

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