Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Oct 24;10(21):e39786.
doi: 10.1016/j.heliyon.2024.e39786. eCollection 2024 Nov 15.

Advancements and challenges in Arabic sentiment analysis: A decade of methodologies, applications, and resource development

Amani A Aladeemy  1   2 , Ali Alzahrani  3 , Mohammad H Algarni  4 , Saleh Nagi Alsubari  1 , Theyazn H H Aldhyani  5 , Sachin N Deshmukh  1 , Osamah Ibrahim Khalaf  6 , Wing-Keung Wong  7 , Sameer Aqburi  8
Affiliations
Review

Advancements and challenges in Arabic sentiment analysis: A decade of methodologies, applications, and resource development

Amani A Aladeemy et al. Heliyon. .

Abstract

The exponential growth of digital information, particularly user-generated content on social media and blogging platforms, has underscored the importance of sentiment analysis (SA). Arabic language sentiment analysis (ASA) involves identifying the orientation of ideas, feelings, emotions, and attitudes within Arabic text to determine whether they convey a positive, negative, or neutral sentiment. This paper presents a comprehensive review of the past decade, focusing on the utilization of SA in the Arabic language. It examines various applications, methodologies, and challenges associated with ASA, highlighting gaps and limitations in existing approaches, lexicons, and annotated datasets. The primary objective of this review is to assist researchers in identifying these gaps and limitations while offering accessible annotated datasets, preprocessing techniques, and procedures. We defined specific criteria for selecting research publications from the last 10 years, including 150 papers in our review process, while excluding earlier publications. The review utilized multiple databases, including Google Scholar, Scopus, and Web of Science. The inherent complexity of the Arabic language, due to its unique traits and diverse dialects, presents significant challenges in ASA. Moreover, the lack of annotated datasets, lexicon resources, and programming tools further complicates sentiment analysis in Arabic. The morphological variations within Arabic make it linguistically challenging. To address these issues, it is crucial to develop additional resources and construct new Arabic sentiment lexicons that account for the various dialects within Modern Standard Arabic (MSA). Our findings reveal that there is no standard public lexicon that adequately enhances the calculation of ASA across different domains, such as e-commerce, politics, public health, and marketing.

Keywords: And lexical tools; Arabic sentiment analysis; Machine learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Classification of Arabic language.
Fig. 2
Fig. 2
General SA process.
Fig. 3
Fig. 3
Sa levels.
Fig. 4
Fig. 4
Number of research articles for ASA.
Fig. 5
Fig. 5
Review method used.
Fig. 6
Fig. 6
Sources of publications.
Fig. 7
Fig. 7
Sentiment analysis approaches.
Fig. 8
Fig. 8
Percentage of research work using a SA technique, b ML techniques, and c DL techniques.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Pang B., Lee L. Opinion mining and sentiment analysis. Foundations and Trends® in information retrieval. 2008;2(1–2):1–135.
    1. Abdulla N.A., Ahmed N.A., Shehab M.A., Al-Ayyoub M. 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) IEEE; 2013, December. Arabic sentiment analysis: lexicon-based and corpus-based; pp. 1–6.
    1. Farra N., Challita E., Abou Assi R., Hajj H. 2010 IEEE International Conference on Data Mining Workshops. IEEE; 2010, December. Sentence-level and document-level sentiment mining for Arabic texts; pp. 1114–1119.
    1. Alwakid G., Osman T., Hughes-Roberts T. Challenges in sentiment analysis for Arabic social networks. Procedia Computer Science. 2017;117:89–100. ‏.
    1. Jeong N., Lee J. An aspect-based review analysis using ChatGPT for the exploration of hotel service failures. Sustainability. 2024;16(4):1640. ‏.

LinkOut - more resources