---
title: "SANA — paper by Abdul-Mageed and Diab (2014)"
description: "The authors build SANA, a subjectivity and sentiment lexicon for Arabic. 32 references, cited by 20 papers here."
authors: ["Muhammad Abdul-Mageed", "Mona Diab"]
year: 2014
venue: "Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)"
source: "https://aclanthology.org/L14-1702/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/mageed-2014-sana-large-scale-multi-genre/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mageed-2014-sana-large-scale-multi-genre/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/) / SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis

# SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis

**Authors:** [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/) and [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/) · **Year:** 2014 · **Venue:** [Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)](https://md.research.elamur.ai/free/en/venues/lrec/) ([LREC 2014](https://md.research.elamur.ai/free/en/venues/lrec/2014/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/L14-1702/) · [PDF](https://aclanthology.org/L14-1702.pdf) · [OpenAlex](https://openalex.org/W2252033489)

**Citations:** 94 citations (OpenAlex, 2026-10-14) · [20](https://md.research.elamur.ai/free/en/papers/mageed-2014-sana-large-scale-multi-genre/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic sentiment research has lacked large polarity lexicons comparable to those available for English, and existing work has focused almost entirely on Modern Standard Arabic. The authors build SANA, a subjectivity and sentiment lexicon for Arabic. It spans several genres: newswire, chat, Twitter and YouTube. It also covers several varieties: MSA, Egyptian (EDA) and Levantine (LDA), with English glosses. Parts of it carry POS tags, diacritics, gender, number and rationality.

SANA was assembled in two ways:
- **Manual labeling:** native speakers labeled two lexicons, SIFAAT (adjectives from the Penn Arabic Treebank) and HUDA (frequent words from Egyptian chat).
- **Automatic acquisition:**
  - PMI-based association scores.
  - Machine translation of English lexicons: SentiWordNet 3, a YouTube lexicon and General Inquirer.
  - Pivoting English glosses through SAMA to collect several Arabic equivalents per entry.
  - Linking to the multi-dialect THARWA dictionary.

The result is a resource with many thousands of entries labeled Pos/Neg/Obj. The authors acknowledge that deduplication and quality control are incomplete and that SANA has not yet been fully tested in downstream sentiment systems. Its value lies in covering dialects and noisy social media text that MSA-only resources miss.

## Key points
- SIFAAT has 3,325 newswire adjectives from PATB parts 1–4 (617 Pos, 550 Neg, 2,158 Obj). Two annotators labeled them independently and then adjudicated disagreements.
- HUDA has 4,905 high-frequency entries from about 11 million Yahoo Maktoob! Egypt chat turns collected in 2008 (1,900 Pos, 1,080 Neg, 1,925 Obj). A single annotator labeled them, and the text was not preprocessed.
- From SWN3's 205,823 unique English entries, the Google Translation API returned Arabic output for 127,291 (61.84%). The YT lexicon (29,991 words from the 229,177-comment YuHC) was also translated.
- ACT entries were matched to SAMA glosses (2,479 matches) and expanded to 6,965 Arabic entries. After 36 entries with conflicting labels were dropped, 6,929 remained. GI produced 4,269 Arabic entries (1,859 Pos, 2,410 Neg).
- THARWA, a dictionary linking Egyptian, Levantine, MSA and English, was merged twice. One instance links through the translated SWN3; the other links through the manually labeled lexicons. This keeps automatically and manually derived material apart.

## Interesting ideas
- Using English glosses as a pivot lets one labeled English word fan out to all target-language words sharing that gloss, and entries that end up with contradictory labels are simply discarded.
- The authors deliberately kept resources derived by hand separate from those derived automatically, building parallel instances rather than one blended lexicon, so quality can be judged by origin.
- Annotators were told to label prior polarity by a word's typical general use and its majority sense, not by personal experience or one particular context.
- For noisy chat and comments, the authors labeled the most frequent raw forms directly and squeezed letter repetitions above two down to two (cooool becomes cool), avoiding preprocessing that would itself make errors.
- PMI against self-assigned clues such as hashtags or emoticons can approximate polarity classes in social media without manual annotation.

## For Mova

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

- **I1** (world): Polarity or class labels could be propagated from one WordNet/FrameNet sense to every lemma attached to it, with automatic removal of lemmas that receive conflicting values.
- **I2** (learn): Every silver annotation could carry a provenance tier (human, witness-parser agreement, LLM teacher), so evaluation and repair rules can weigh or filter by source.
- **I3** (frames): Out-of-context polarity priors built this way could serve as defaults, which irony and implicature detection then overrides when the context contradicts them.
- **I4** (tokens): A repeated-letter normalization step plus a frequency-ranked raw-form list could help the tokenizer and lemmatizer handle informal English without a fragile pipeline.
- **I5** (learn): Explicit surface cues could act as weak labels for inducing lexical priors, checked against negative controls before being trusted.

*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

- [George Washington University](https://md.research.elamur.ai/free/en/universities/george-washington-university/) — University, Washington, United States · 51 works · 8,148 citations
- [Indiana University Bloomington](https://md.research.elamur.ai/free/en/universities/indiana-university-bloomington/) — University, Bloomington, United States · 40 works · 4,003 citations
- [Indiana University](https://md.research.elamur.ai/free/en/universities/indiana-university/) — University, Bloomington, United States · 42 works · 3,981 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,649 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 930 works

🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 1,035 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 3,066 works

## Cite

Cite as: Muhammad Abdul-Mageed and Mona Diab. 2014. SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14).

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

```bibtex
@inproceedings{abdulmageed2014sana,
  title = {{SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis}},
  author = {Muhammad Abdul-Mageed and Mona Diab},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  doi = {10.63317/4xff9iiz2q7b},
  url = {https://doi.org/10.63317/4xff9iiz2q7b}
}
```

- [← previous Automatic Transliteration of Romanized Dialectal Arabic](https://md.research.elamur.ai/free/en/papers/badrashiny-2014-automatic-transliteration-romanized-dialectal-arabic/)
- [next → Dependency-based Discourse Parser for Single-Document Summarization](https://md.research.elamur.ai/free/en/papers/yoshida-2014-dependency-based-discourse-parser-single/)
- [↑ back to papers of 2014](https://md.research.elamur.ai/free/en/papers/2014/page/2/)

## References

32 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Abbasi, A., Chen, H., and Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Inf. Syst., 26:1–34. [find](https://scholar.google.com/scholar?q=Abbasi%2C+A%2E%2C+Chen%2C+H%2E%2C+and+Salem%2C+A%2E+%282008%29%2E+Sentiment+analysis+in+multiple+languages%3A+Feature+selection+for+opinion+classification+in+web+forums%2E+ACM+Trans%2E+Inf%2E+Syst%2E%2C+26%3A1%E2%80%9334%2E)
2. Abdul-Mageed, M. and Diab, M. (2011). [Subjectivity and sentiment annotation of modern standard arabic newswire](https://md.research.elamur.ai/free/en/papers/mageed-2011-subjectivity-sentiment-annotation-modern-standard/). In Proceedings of the 5th Linguistic Annotation Workshop, pages 110–118, Portland, Oregon, USA, June. Association for Computational Linguistics.
3. Abdul-Mageed, M. and Diab, M. (2012a). [AWATIF: A multi-genre corpus for modern standard arabic subjectivity and sentiment analysis](https://md.research.elamur.ai/free/en/papers/mageed-2012-awatif-multi-genre-corpus-modern/). In Proceedings of LREC, volume 12.
4. Abdul-Mageed, M. and Diab, M. (2012b). Toward building a large-scale arabic sentiment lexicon. In Proceedings of the 6th International Global WordNet Conference, Matsue, Japan, January. [find](https://scholar.google.com/scholar?q=Abdul%2DMageed%2C+M%2E+and+Diab%2C+M%2E+%282012b%29%2E+Toward+building+a+large%2Dscale+arabic+sentiment+lexicon%2E+In+Proceedings+of+the+6th+International+Global+WordNet+Conference%2C+Matsue%2C+Japan%2C+January%2E)
5. Abdul-Mageed, M., Diab, M., and Korayem, M. (2011a). [Subjectivity and sentiment analysis of modern standard arabic](https://md.research.elamur.ai/free/en/papers/mageed-2011-subjectivity-sentiment-analysis-modern-standard/). In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 587–591, Portland, Oregon, USA, June. Association for Computational Linguistics.
6. Abdul-Mageed, M., Korayem, M., and YoussefAgha, A. (2011b). ”yes we can?”: Subjectivity annotation and tagging for the health domain. In Proceedings of RANLP2011. [find](https://scholar.google.com/scholar?q=Abdul%2DMageed%2C+M%2E%2C+Korayem%2C+M%2E%2C+and+YoussefAgha%2C+A%2E+%282011b%29%2E+%E2%80%9Dyes+we+can%3F%E2%80%9D%3A+Subjectivity+annotation+and+tagging+for+the+health+domain%2E+In+Proceedings+of+RANLP2011%2E)
7. Abdul-Mageed, M., Kübler, S., and Diab, M. (2012). [Samar: a system for subjectivity and sentiment analysis of arabic social media](https://md.research.elamur.ai/free/en/papers/mageed-2012-samar-system-subjectivity-sentiment-analysis/). In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA ’12, pages 19–28, Stroudsburg, PA, USA. Association for Computational Linguistics.
8. Abdul-Mageed, M., Diab, M., and Kbler, S. (2014). Samar: Subjectivity and sentiment analysis for arabic social media. Computer Speech Language, 28(1):20 – 37. [↗](https://doi.org/10.1016/j.csl.2013.03.001)
9. Baccianella, S., Esuli, A., and Sebastiani, F. (2010). [Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining](https://md.research.elamur.ai/free/en/papers/baccianella-2010-sentiwordnet-3-0-enhanced-lexical/). In Seventh conference on International Language Resources and Evaluation, Malta. Retrieved May, volume 25, page 2010.
10. Buckwalter, T. (2002). Arabic morphological analyzer version 2.0. [find](https://scholar.google.com/scholar?q=Buckwalter%2C+T%2E+%282002%29%2E+Arabic+morphological+analyzer+version+2%2E0%2E)
11. Linguistic Data Consortium, 2004. [find](https://scholar.google.com/scholar?q=Linguistic+Data+Consortium%2C+2004%2E)
12. Christopherson, K. M. (2007). The positive and negative implications of anonymity in internet social interactions:on the internet, nobody knows youre a dog. Computers in Human Behavior, 23(6):3038–3056. [↗](https://doi.org/10.1016/j.chb.2006.09.001)
13. Church, K. and Hanks, P. (1989). [Word association norms, mutual information and lexicography](https://md.research.elamur.ai/free/en/papers/church-1990-word-association-norms-mutual-information/). In Proceedings of the 27th Annual Conference of the ACL, pages 76–83. Association for Computational Linguistics. [↗](https://doi.org/10.5555/89086.89095)
14. Dave, K., Lawrence, S., and Pennock, D. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web, pages 519–528. ACM. [find](https://scholar.google.com/scholar?q=Dave%2C+K%2E%2C+Lawrence%2C+S%2E%2C+and+Pennock%2C+D%2E+%282003%29%2E+Mining+the+peanut+gallery%3A+Opinion+extraction+and+semantic+classification+of+product+reviews%2E+In+Proceedings+of+the+12th+international+conference+on+Wor)
15. Diab, M., Al-Badrashiny, M., Aminian, M., Attia, M., Dasigi, P., Elfardy, H., Eskander, R., Habash, N., Hawwari, A., Salloum, W., and Yusef, M. (2014). A large scale three-way egyptian arabic dictionary. [find](https://scholar.google.com/scholar?q=Diab%2C+M%2E%2C+Al%2DBadrashiny%2C+M%2E%2C+Aminian%2C+M%2E%2C+Attia%2C+M%2E%2C+Dasigi%2C+P%2E%2C+Elfardy%2C+H%2E%2C+Eskander%2C+R%2E%2C+Habash%2C+N%2E%2C+Hawwari%2C+A%2E%2C+Salloum%2C+W%2E%2C+and+Yusef%2C+M%2E+%282014%29%2E+A+large+scale+three%2Dway+egyptian+arabic+dictiona)
16. Esuli, A. and Sebastiani, F. (2005). Determining the semantic orientation of terms through gloss classification. In Proceedings of the 14th ACM international conference on Information and knowledge management, pages 617– 624. ACM. [find](https://scholar.google.com/scholar?q=Esuli%2C+A%2E+and+Sebastiani%2C+F%2E+%282005%29%2E+Determining+the+semantic+orientation+of+terms+through+gloss+classification%2E+In+Proceedings+of+the+14th+ACM+international+conference+on+Information+and+knowledge+ma)
17. Fellbaum, C. (1998). WordNet: An electronic lexical database. The MIT press. [find](https://scholar.google.com/scholar?q=Fellbaum%2C+C%2E+%281998%29%2E+WordNet%3A+An+electronic+lexical+database%2E+The+MIT+press%2E)
18. Graff, D., Maamouri, M., Bouziri, B., Krouna, S., Kulick, S., and Buckwalter, T. (2009). Standard Arabic Morphological Analyzer (SAMA) Version 3.1. Linguistic Data Consortium LDC2009E73. [find](https://scholar.google.com/scholar?q=Graff%2C+D%2E%2C+Maamouri%2C+M%2E%2C+Bouziri%2C+B%2E%2C+Krouna%2C+S%2E%2C+Kulick%2C+S%2E%2C+and+Buckwalter%2C+T%2E+%282009%29%2E+Standard+Arabic+Morphological+Analyzer+%28SAMA%29+Version+3%2E1%2E+Linguistic+Data+Consortium+LDC2009E73%2E)
19. Hatzivassiloglou, V. and McKeown, K. (1997). Predicting the semantic orientation of adjectives. In ACL-EACL, pages 174–181. [find](https://scholar.google.com/scholar?q=Hatzivassiloglou%2C+V%2E+and+McKeown%2C+K%2E+%281997%29%2E+Predicting+the+semantic+orientation+of+adjectives%2E+In+ACL%2DEACL%2C+pages+174%E2%80%93181%2E)
20. Heise, D. R. (2007). Expressive order: Confirming sentiments in social actions. Springer. [↗](https://doi.org/10.1007/978-0-387-38179-4)
21. Herring, S. C. (2007). A faceted classification scheme for computer-mediated discourse. Language@ internet, 4(1):1–37. [find](https://scholar.google.com/scholar?q=Herring%2C+S%2E+C%2E+%282007%29%2E+A+faceted+classification+scheme+for+computer%2Dmediated+discourse%2E+Language%40+internet%2C+4%281%29%3A1%E2%80%9337%2E)
22. Kamps, J., Marx, M., Mokken, R., and De Rijke, M. (2004). Using wordnet to measure semantic orientations of adjectives. [find](https://scholar.google.com/scholar?q=Kamps%2C+J%2E%2C+Marx%2C+M%2E%2C+Mokken%2C+R%2E%2C+and+De+Rijke%2C+M%2E+%282004%29%2E+Using+wordnet+to+measure+semantic+orientations+of+adjectives%2E)
23. Kim, S. and Hovy, E. (2004). [Determining the sentiment of opinions](https://md.research.elamur.ai/free/en/papers/kim-2004-determining-sentiment-opinions/). In Proceedings of the 20th International Conference on Computational Linguistics, pages 1367–1373.
24. Landis, J. and Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1):159. [↗](https://doi.org/10.2307/2529310)
25. Maamouri, M., Bies, A., Buckwalter, T., and Mekki, W. (2004). The penn arabic treebank: Building a large-scale annotated arabic corpus. In NEMLAR Conference on Arabic Language Resources and Tools, pages 102– 109. [find](https://scholar.google.com/scholar?q=Maamouri%2C+M%2E%2C+Bies%2C+A%2E%2C+Buckwalter%2C+T%2E%2C+and+Mekki%2C+W%2E+%282004%29%2E+The+penn+arabic+treebank%3A+Building+a+large%2Dscale+annotated+arabic+corpus%2E+In+NEMLAR+Conference+on+Arabic+Language+Resources+and+Tools%2C+pag)
26. Maamouri, M., Graff, D., Bouziri, B., Krouna, S., and Kulick, S. (2010). Ldc standard arabic morphological analyzer (sama) v. 3.1. LDC Catalog No. LDC2010L01. ISBN, pages 1–58563. [find](https://scholar.google.com/scholar?q=Maamouri%2C+M%2E%2C+Graff%2C+D%2E%2C+Bouziri%2C+B%2E%2C+Krouna%2C+S%2E%2C+and+Kulick%2C+S%2E+%282010%29%2E+Ldc+standard+arabic+morphological+analyzer+%28sama%29+v%2E+3%2E1%2E+LDC+Catalog+No%2E+LDC2010L01%2E+ISBN%2C+pages+1%E2%80%9358563%2E)
27. Miller, G. (1995). [Wordnet: a lexical database for english](https://md.research.elamur.ai/free/en/papers/miller-1994-wordnet/). Communications of the ACM, 38(11):39–41.
28. Mohammad, S. M. (2012). # emotional tweets. In Proceedings of the First Joint Conference on Lexical and Computational Semantics-Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pages 246–255. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mohammad%2C+S%2E+M%2E+%282012%29%2E+%23+emotional+tweets%2E+In+Proceedings+of+the+First+Joint+Conference+on+Lexical+and+Computational+Semantics%2DVolume+1%3A+Proceedings+of+the+main+conference+and+the+shared+task%2C+and+Vo)
29. Mullen, T. and Collier, N. (2004). Sentiment analysis using support vector machines with diverse information sources. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP) Conference, pages 412–418, Barcelona, Spain. [find](https://scholar.google.com/scholar?q=Mullen%2C+T%2E+and+Collier%2C+N%2E+%282004%29%2E+Sentiment+analysis+using+support+vector+machines+with+diverse+information+sources%2E+In+Proceedings+of+the+Empirical+Methods+in+Natural+Language+Processing+%28EMNLP%29+Con)
30. Stone, P. J., Dunphy, D. C., and Smith, M. S. (1966). The general inquirer: A computer approach to content analysis. [find](https://scholar.google.com/scholar?q=Stone%2C+P%2E+J%2E%2C+Dunphy%2C+D%2E+C%2E%2C+and+Smith%2C+M%2E+S%2E+%281966%29%2E+The+general+inquirer%3A+A+computer+approach+to+content+analysis%2E)
31. Turney, P. D. and Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4):315–346. [↗](https://doi.org/10.48550/arxiv.cs/0309034)
32. Turney, P. (2002). [Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews](https://md.research.elamur.ai/free/en/papers/turney-2002-thumbs-up-or-thumbs-down/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL’02). [↗](https://doi.org/10.48550/arxiv.cs/0212032)

*Updated 2 November 2026*