A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions
Not on the map. Why?
Abstract (arXiv)
Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent years. However, the majority of approaches are aimed at the English language, and research towards Arabic sentiment analysis remains relatively unexplored. This paper presents a comprehensive and contemporary survey of Arabic Sentiment Analysis, identifies the challenges and limitations of existing literature in this field and presents avenues for future research. We present a systematic review of Arabic sentiment analysis methods, focusing specifically on research utilizing deep learning. We then situate Arabic Sentiment Analysis within the broader context, highlighting research gaps in Arabic sentiment analysis as compared to general sentiment analysis. Finally, we outline the main challenges and promising future directions for research in Arabic sentiment analysis.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 71 references that are in the map. First posted on arXiv 6 Feb 2025.
Builds on
25 of 71 references.
References · 25
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2022Emergent Abilities of Large Language Modelscited by 2,512
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
- 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 362
- 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 268
- 2020ARBERT & MARBERT: Deep Bidirectional Transformers for Arabiccited by 166
- 2019Word Embeddings: A Surveycited by 45
- 2014LABR: A Large Scale Arabic Sentiment Analysis Benchmarkcited by 22
- 2021Evaluating Various Tokenizers for Arabic Text Classificationcited by 14
- 2020A Multitask Learning Approach for Diacritic Restorationcited by 12
13 more
- 2021Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Languagecited by 12
- 2019ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweetscited by 11
- 2018SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysiscited by 6
- 2022A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectivescited by 6
- 2017Comparative Opinion Mining: A Reviewcited by 5
- 2022Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classificationcited by 3
- 2023SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasmcited by 3
- 2021Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identificationcited by 2
- 2022A Benchmark Study of Contrastive Learning for Arabic Social Meaningcited by 2
- 2023Arabic Sentiment Analysis with Noisy Deep Explainable Modelcited by 2
- 2021Arabic aspect sentiment polarity classification using BERTcited by 1
- 2021sarcasm detection and quantification in arabic tweetscited by 1
- 2024Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysiscited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.