Arabic sentiment analysis: studies, resources, and tools

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作者
Imane Guellil
Faical Azouaou
Marcelo Mendoza
机构
[1] Ecole nationale Supérieure d’Informatique,Laboratoire des Méthodes de Conception des Systèmes
[2] Universidad Técnica Federico Santa María,Department of Informatics
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Arabic sentiment analysis; Sentiment lexicon; Corpus lexicon; Machine learning; Deep learning; Word embeddings;
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摘要
To determine whether a document or a sentence expresses a positive or negative sentiment, three main approaches are commonly used: the lexicon-based approach, corpus-based approach, and a hybrid approach. The study of sentiment analysis in English has the highest number of sentiment analysis studies, while research is more limited for other languages, including Arabic and its dialects. Lexicon based approaches need annotated sentiment lexicons (containing the valence and intensity of its terms and expressions). Corpus-based sentiment analysis requires annotated sentences. One of the significant problems related to the treatment of Arabic and its dialects is the lack of these resources. We present in this survey the most recent resources and advances that have been done for Arabic sentiment analysis. This survey presents recent work (where the majority of these works are between 2015 and 2019). These works are classified by category (survey work or contribution work). For contribution work, we focus on the construction of sentiment lexicon and corpus. We also describe emergent trends related to Arabic sentiment analysis, principally associated with the use of deep learning techniques.
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