---
title: "Using objective words in the reviews to improve the colloquial arabic sentiment — Al-Harbi 2017"
description: "The author builds two lexicons. 95.6% accuracy. 44 references, cited by 2 papers here."
authors: ["Omar Al-Harbi"]
year: 2017
venue: "arXiv:1709.08521 · International Journal on Natural Language Computing (IJNLC) Vol. 6, No.3, June 2017"
source: "https://arxiv.org/abs/1709.08521"
doi: "10.5121/ijnlc"
arxiv: "1709.08521"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/al-harbi-2017-using-objective-words-reviews-improve/"
---
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# Using objective words in the reviews to improve the colloquial arabic sentiment analysis

**Authors:** [Omar Al-Harbi](https://md.research.elamur.ai/free/en/authors/omar-al-harbi/) · **Year:** 2017 · **Venue:** arXiv:1709.08521 · International Journal on Natural Language Computing (IJNLC) Vol. 6, No.3, June 2017 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://doi.org/10.5121/ijnlc) · [PDF](https://arxiv.org/pdf/1709.08521) · [DOI 10.5121/ijnlc](https://doi.org/10.5121/ijnlc) · [arXiv 1709.08521](https://arxiv.org/abs/1709.08521) · [OpenAlex](https://openalex.org/W3100610995)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/al-harbi-2017-using-objective-words-reviews-improve/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
Sentiment analysis of Arabic is hard because people write reviews in colloquial dialects that have no fixed spelling, morphology or vocabulary. Most work focuses on MSA. This paper looks at Jordanian colloquial reviews and asks whether "objective" words help with polarity classification. These are words with no explicit sentiment, which most models discard. The author builds two lexicons. The first holds colloquial sentiment words and multiword compound phrases. The second holds objective words, each given a sentiment-tendency value by an estimation method based on how the words appear in reviews. Features drawn from both lexicons are fed to an SVM, alone and combined with different n-gram models, to label each review as positive or negative. The reviews were gathered by hand from the JEERAN website. The combined approach beats two baseline models and reaches 95.6% accuracy. The work matters for two reasons: dialect resources for Arabic are scarce, and it questions the common practice, following Pang and Lee, of removing objective content before classifying sentiment.

## Key points
- Target: binary (positive/negative) polarity of Jordanian colloquial Arabic reviews, collected manually from JEERAN.
- Two hand-built resources:
  - a lexicon of colloquial sentiment words plus compound phrases;
  - a lexicon of objective words, each with an estimated sentiment-tendency score.
- Classifier: SVM (LIBSVM is cited). Lexicon-derived features are tested alone and together with n-gram models.
- Best reported accuracy is 95.6%, higher than two baseline models.
- Motivation comes from English work by Hung and Lin. They noted that over 90% of SentiWordNet entries are objective. Re-scoring those entries by their occurrence in positive and negative reviews improved accuracy by 4.10%.

## Interesting ideas
- "Neutral" words are not necessarily neutral within a domain: a word's distribution across positive and negative reviews can reveal a hidden lean, and that lean can be measured instead of assumed to be zero.
- Treat multiword compound phrases as single sentiment units in the lexicon, rather than scoring their parts separately, because new colloquial compounds carry meanings their components do not.
- Hybrid design: a lexicon supplies interpretable features, and a learned classifier decides. This addresses the opposite biases of each approach: learners are conservative on positives full of factual statements, and lexicons over-react to a few positive words.
- Dialects keep generating new clitics, negation forms and stop-words, so a fixed stop-list or negation list goes stale and silently damages downstream analysis.

## For Mova

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

- **I1** (world): Domain-conditioned evaluative leanings could be stored as revisable values on ontology entries (e.g., a product feature that tends to appear in complaints). Each value would stay traceable to the evidence that produced it.
- **I2** (frames): Evaluative idioms could be recognized as one unit over a UD subtree and given one attitude in the pragmatics layer. This avoids summing the polarity of each word.
- **I3** (learn): When evaluating any lexicon-driven component, compare it against both a pure-lexicon and a pure-learned baseline. This shows which bias the component actually fixes.
- **I4** (tokens): Negation and function-word inventories should be versioned, extensible data with coverage checks on new text, not hard-coded lists.

*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](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [🐪 Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/)

## Institutions

Also on the byline: [Centre de Recherche en Linguistique Appliquée](https://ror.org/048z48z46).

## Cite

Cite as: Omar Al-Harbi. 2017. Using objective words in the reviews to improve the colloquial arabic sentiment analysis. arXiv:1709.08521 · International Journal on Natural Language Computing (IJNLC) Vol. 6, No.3, June 2017.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{alharbi2017using,
  title = {{Using objective words in the reviews to improve the colloquial arabic sentiment analysis}},
  author = {Omar Al-Harbi},
  year = {2017},
  doi = {10.5121/ijnlc},
  eprint = {1709.08521},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.5121/ijnlc}
}
```

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## References

44 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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