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Sentiment analysis for Arabic language: A brief survey of approaches and techniques

Paper · 2018 · Mo'ath Alrefai, Hossam Faris and Ibrahim Aljarah

Summary

This survey looks at why sentiment analysis for Arabic lags behind English and how existing studies have tried to close the gap. The authors gathered Arabic sentiment work from Springer, IEEE, ACM and earlier surveys, with the collection finished in August 2017. They sorted it into three families: machine learning, lexicon-based and hybrid. They also describe a generic four-stage pipeline: data collection and annotation, cleaning and preprocessing, classification, and evaluation. The paper lists language-specific obstacles, including dialect variety, spelling variation, negation, gender and number agreement on verbs, names that look like adjectives, and idioms. It then catalogues the preprocessing tricks used to handle them. Machine learning dominates the field, with SVM and NB as the usual classifiers. Hybrids are reported to work best but are rarely studied. Finally, the authors sketch their own model, in which an evolutionary algorithm tunes SVM parameters and picks features at the same time. The survey matters because it maps a resource-poor, morphologically rich language and shows where datasets and lexicons are still missing.

Key points

  • Publication counts by approach: 45 machine learning papers, 25 lexicon-based papers and 12 hybrid papers. Research on the topic starts around 2006 and grows quickly in later years.
  • Two public datasets are highlighted:
    • OCA, a corpus of 250 positive and 250 negative MSA reviews from blogs and web pages.
    • ASTD, about 10k tweets labelled as objective or subjective (positive, negative, mixed).
  • Common preprocessing steps include:
    • letter normalization and collapsing of elongated letters;
    • removal of stop words, punctuation and diacritics;
    • stemming, often with the Khoja stemmer;
    • converting emoticons to words and mapping dialect words to MSA;
    • negation and intensification detection, and POS tagging.
  • Typical tools are RapidMiner, MATLAB, R and WEKA. The proposed SVM + evolutionary-algorithm model is planned to be implemented in EvoloPy.
  • Trade-offs between the families:
    • Machine learning is domain-dependent.
    • Lexicons are domain-independent but small, scarce and weak on idioms.
    • Arabic lacks equivalents of WordNet, SentiWordNet and MPQA.

Interesting ideas

  • I1. Standard pipelines throw away negation words as stop words, which flips the polarity of a sentence; negators and intensifiers should be kept as explicit operators that reverse or scale the polarity of nearby words.
  • I2. A personal name can share its exact surface form with an evaluative adjective (Jameelah vs. "beautiful"), so a lexicon lookup alone can invent sentiment where none exists.
  • I3. Idioms can express a clear negative opinion while containing no negative word, so polarity must sometimes be stored at the level of the whole phrase.
  • I4. Elongated letters and emoticons are normalized away during preprocessing, yet the survey treats them as signals of intensity and meaning worth translating into words.
  • I5. The proposed model uses an evolutionary search over classifier hyperparameters and feature subsets together, then inspects which features survive to learn what matters for the task.

For Mova

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

  • I1 (frames): Attach negation and degree modifiers from the UD tree as scope markers on frames, so that "not good" yields a negated evaluation instead of a positive one.
  • I2 (world): Resolve entity status before applying evaluative lexicons, so names like "Grace" or "Hope" become referents in the world rather than carrying positive polarity.
  • I3 (frames): Keep a multiword idiom table that maps fixed expressions directly to evaluation frames, which also feeds implicature and irony detection.
  • I4 (tokens): Collapse forms like "sooooo" to the lemma "so" but record an intensity feature on the token, and map emoticons to lexical items instead of deleting them.
  • I5 (learn): Apply a similar search to the perceptron parser's feature templates and hyperparameters, and treat the surviving templates as an explainable report checked against negative controls.

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 JordanAmman · Jordan14 works · 171 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

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

Cite

Cite as Mo'ath Alrefai, Hossam Faris and Ibrahim Aljarah. 2018. Sentiment analysis for Arabic language: A brief survey of approaches and techniques. arXiv:1809.02782.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.1809.02782 · arXiv 1809.02782

@misc{alrefai2018sentiment,
  title = {{Sentiment analysis for Arabic language: A brief survey of approaches and techniques}},
  author = {Mo'ath Alrefai, Hossam Faris and Ibrahim Aljarah},
  year = {2018},
  doi = {10.48550/arxiv.1809.02782},
  eprint = {1809.02782},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1809.02782}
}

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