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A review of sentiment analysis research in Arabic language

Summary

This survey asks why Arabic sentiment analysis (ASA) still lags behind English work despite a fast-growing Arabic web. The authors follow Kitchenham-style systematic review guidelines. They pose four research questions: the main ASA challenges, whether existing resources address them, which monolingual methods exist, and how English resources were transferred through machine translation or transfer learning. More than 100 papers from 2006–2019 were collected from Springer, Elsevier, IEEE, ACM and ACL. Similar queries for English return about 12k papers.

The review covers several topics:

  • challenges, both general (domain dependency, sarcasm, spam) and Arabic-specific;
  • corpora and lexica;
  • pre-processing, features and classifiers.

The main finding is that classification quality depends heavily on resource quality. Most resources cover only Modern Standard Arabic (MSA), while social media is dominated by dialects and Arabizi. Translating English resources did not substantially advance the field. The authors recommend building resources that cover dialects and Arabizi, and moving from word-level to concept-based analysis. This matters for anyone building NLP for under-resourced, morphologically rich languages with strong variation between formal and informal registers.

Key points

  • Arabic is reported as the fourth most used Internet language, with about 185 million users (4.8% of users) and 1616.4% user growth over seventeen years.
  • Per year, MSA is studied far more than dialects or Arabizi. Dialect work grows from 2014, and deep learning appears in ASA only from 2017.
  • About 80% of dialectal sentiment resources target Egyptian Arabic, which hurts performance in multidialect settings.
  • NB and SVM are the most common classifiers. Approaches split into lexicon-based, corpus-based and hybrid.
  • The authors propose an Arabic SenticNet, citing SenticNet's availability in 40 languages and its success for Chinese. They also point to SemEval-2017 Task 4 and SemEval-2018 Task 1 as sources of dialectal data.

Interesting ideas

  • I1. Heavy pre-processing can destroy meaning: stemming merges Arabic words for "play" and "tamper" into one stem, and deleting Arabizi drops real sentiment cues. Normalisation should therefore be conservative and keep information. — For Mova (tokens): Keep lemma and morphology decisions reversible, and log what each normalisation merges, so later stages never lose distinctions they cannot recover.
  • I2. Sentiment should be attached to concepts and multi-word expressions (idioms, prayers, slang) rather than single keywords, because the meaning of the phrase is not the sum of its words. — For Mova (frames): Treat idioms and fixed expressions as single frame-evoking units, so evaluative stance is assigned to the expression as a whole.
  • I3. The SenticNet assumption is that semantically related concepts share sentiment, so polarity can spread along semantic links from a seed set. — For Mova (world): Values or attitudes on a few WordNet/FrameNet concepts could be propagated through the knowledge graph, recording an explicit trace of which link supplied each value.
  • I4. A resource built for one variety (MSA, or one dialect) performs weakly on mixed real-world input, so evaluation must match the target distribution. — For Mova (learn): Add held-out test slices from other registers and genres as controls, so gains on one variety are not mistaken for general gains.
  • I5. Translating resources across structurally different languages carries errors into the target task, which argues for building meaning in the target language itself. — For Mova (back): Translation should go through the meaning graph rather than surface-to-surface mapping, with checks that sentiment and stance survive the round trip.

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

Fields: Linguistics & languages

Cited by

14 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 7 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1016/j.future.2020.05.034 · arXiv 2005.12240

@misc{hajhmida2020review,
  title = {{A review of sentiment analysis research in Arabic language}},
  author = {HajHmida;, Oumaima Oueslati; Erik Cambria; Moez Ben and Ounelli, Habib},
  year = {2020},
  doi = {10.1016/j.future.2020.05.034},
  eprint = {2005.12240},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1016/j.future.2020.05.034}
}

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