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A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions

Paper · 2025 · Zhiqiang Shi and Ruchit Agrawal

Summary

This survey targets Arabic sentiment analysis (ASA). The authors argue that earlier ASA reviews mostly predate deep learning and give little analysis of open gaps. They review recent work, mainly deep-learning approaches, and sort it along three axes: modality (unimodal or multimodal), granularity (coarse classification or finer tasks such as ABSA and MAST) and context (document, sentence or aspect level). For each task they first describe progress in general, English-dominated sentiment analysis and then the Arabic counterpart, which exposes where Arabic lags behind. Tables list ASA datasets and, for each method, its contribution, reported scores and limitations. A case study argues that classic sentiment lexicons still help neural models through feature augmentation, preprocessing, weighting and interpretability. The survey's main finding is that most Arabic work remains document-level classification. Dialect handling and Arabic-specific tokenization are the central challenges, and multimodal, fine-grained, LLM-based and explainable ASA are largely unexplored. This gives researchers a structured map of the field for a language with over 372 million speakers.

Key points

  • Two lexicons are described. Arabic Senti-Lexicon has 3880 synsets annotated with POS, polarity and inflected forms. ArSenL has 157,969 synsets and was built from English WordNet, Arabic WordNet, English SentiWordNet and SAMA.
  • The dataset table covers LABR and ASTD (document-level sentiment classification), ArSentD-LEV (Levantine, with ABSA and MAST labels), ArSarcasm, ArSarcasm-v2 and Arsen-20, plus a text/audio/video Arabic multimodal dataset (Haouhat et al., 2023).
  • Reported task-specific results include:
    • a CNN over Arabic word embeddings: LABR 89.6% and ASTD 79.07%;
    • on ASTD: SVM 51.7% vs RNTN 58.5%;
    • a language-independent CNN: 67.93% on ASTD;
    • an explainable framework: 88.0% on LABR.
  • Dialect variation is addressed by models such as ARBERT and MARBERT, pre-trained on dialectal Arabic, and by transfer from MSA or multilingual models like mBERT.
  • Tokenization trends include:
    • morphological analysis (MADAMIRA);
    • dialect lexicons (CALIMA-Star);
    • contextual tokenization (AraBERT);
    • careful handling of diacritics, either keeping them or normalizing them without losing meaning.

Interesting ideas

  • I1. A sentiment lexicon can serve as a transparent side channel next to a learned model, so that each term's polarity can be traced into the final judgement and checked with tools like LIME.
  • I2. The task space is laid out on three independent axes (modality, granularity, context), so that coverage gaps become visible as empty cells.
  • I3. Every surveyed method gets an explicit "limitations" entry next to its scores, such as being tested on one dataset only or never compared against stronger architectures.
  • I4. Diacritics can either be kept or normalized, but the normalized form should not lose the meaning-bearing information.
  • I5. The survey suggests that sentiment models output their reasoning steps before the final polarity, borrowing from interpretable QA. It also notes that feature-based systems sometimes beat pre-trained models.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (world): A compiled polarity/value layer attached to WordNet-linked lemmas could explain evaluative states of entities. Polarity should be propagated over the UD tree and frames (negation, scope) rather than summed as a bag of words.
  • I2 (learn): The evaluation suite could be indexed the same way, for example by text genre, level of analysis and unit (sentence, paragraph, whole tale), to show which combinations have no tests yet.
  • I3 (learn): Each reported metric could carry a mandatory note on what was not controlled, pairing results with the negative controls Mova already runs.
  • I4 (tokens): The en crate could store both the surface form and a normalized form (spelling variants, informal or social-media spellings) on each token. The lemmatizer would then normalize without discarding the original evidence.
  • I5 (answer): Readers answering questions about a character's feelings in fables could cite the frames and states that justify the answer. Simple feature baselines should be kept as reference points.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of EdinburghEdinburgh · United Kingdom359 works · 77,352 citations🎓University of BirminghamBirmingham · United Kingdom25 works · 62,564 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 3Research subjects and languages the work is about.Sentiment & emotion · 972 worksSemitic languages · 2,908 worksVision & language · 657 works

Cite

Cite as Zhiqiang Shi and Ruchit Agrawal. 2025. A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions. Findings of the Association for Computational Linguistics: NAACL 2025.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.findings-naacl.208

@inproceedings{shi2025comprehensive,
  title = {{A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions}},
  author = {Zhiqiang Shi and Ruchit Agrawal},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-naacl.208},
  url = {https://doi.org/10.18653/v1/2025.findings-naacl.208}
}

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