--- title: "Subjectivity and Sentiment Analysis of Modern Standard Arabic" description: "The paper addresses the lack of subjectivity and sentiment analysis (SSA) resources and systems for… 12 references, cited by 63 papers here." authors: ["Muhammad Abdul-Mageed", "Mona Diab", "Mohammed Korayem"] year: 2011 venue: "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies" source: "https://aclanthology.org/P11-2103/" licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/mageed-2011-subjectivity-sentiment-analysis-modern-standard/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mageed-2011-subjectivity-sentiment-analysis-modern-standard/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2011](https://md.research.elamur.ai/free/en/papers/2011/) / Subjectivity and Sentiment Analysis of Modern Standard Arabic # Subjectivity and Sentiment Analysis of Modern Standard Arabic **Authors:** [Muhammad Abdul-Mageed](https://md.research.elamur.ai/free/en/authors/muhammad-abdul-mageed/), [Mona Diab](https://md.research.elamur.ai/free/en/authors/mona-diab/) and Mohammed Korayem · **Year:** 2011 · **Venue:** [Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2011](https://md.research.elamur.ai/free/en/venues/acl/2011/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/) **Links:** [Publisher's page](https://aclanthology.org/P11-2103/) · [PDF](https://aclanthology.org/P11-2103.pdf) · [OpenAlex](https://openalex.org/W2124752409) **Citations:** 244 citations (OpenAlex, 2026-10-14) · [63](https://md.research.elamur.ai/free/en/papers/mageed-2011-subjectivity-sentiment-analysis-modern-standard/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary The paper addresses the lack of subjectivity and sentiment analysis (SSA) resources and systems for morphologically rich languages, focusing on Modern Standard Arabic (MSA) newswire. The authors built a sentence-level SSA corpus on top of the Penn Arabic Treebank (PATB). Two native speakers labeled each sentence as objective or as positive, negative, or neutral subjective. They also compiled a hand-made polarity lexicon of adjectives. Using gold PATB tokenization, they compared three ways of normalizing stem words: Surface, Lemma, and Stem. They then added Arabic inflectional features and several language-independent features. Classification runs in two binary steps with linear SVMs: subjective versus objective, then positive versus negative. The Stem setting worked best for both tasks, and morphological features gave further small gains. The domain-specific adjective lexicon had by far the largest effect on sentiment. The work shows that explicitly modeling morphology helps SSA in a language like Arabic, and it contributes rare annotated resources for the field. ## Key points - **Corpus:** 2855 sentences from the first 400 documents of PATB Part 1 V 3.0, which is about 54.5% of that part. - Inter-annotator agreement was 88.06%. - Class counts: 1281 OBJ, 491 S-POS, 689 S-NEG, and 394 S-NEUT. - Each sentence also carries a newswire domain label. - **Polarity lexicon:** 3982 newswire-domain adjectives, each tagged positive, negative, or neutral. - **Setup:** - Text uses the PATB+Al tokenization scheme. - Classifier is SVM light with a linear kernel and binary presence vectors over N-grams up to 4. - Morphological features cover person, state, gender, tense, aspect, and number. No POS tags or diacritics are used. - **Subjectivity results:** - On DEV, Stem reached 73.17% F, ahead of Lemma (72.74) and Surface (71.97). - The best TEST result was 71.54% F (Stem+Morph+ADJ), against a 55.13% majority baseline. - **Sentiment results:** - On DEV, Stem reached 56.87% F, versus 55.01 for Lemma and 52.53 for Surface. - Adding the ADJ lexicon features raised results to 90.93% F on DEV and 95.52% F on TEST, against a 58.38% baseline. ## Interesting ideas - **Annotating on top of an existing treebank.** Adding the sentiment layer to PATB sentences gives access to gold tokenization and morphology for free. This separates the effect of preprocessing from tagger errors, which is a useful design for any low-resource language that has a treebank. - **Comparing normalization depth as an experimental variable.** The authors tested surface forms, dictionary lemmas, and crude IR-style stems that may not be real words. The crude stems won, which suggests that for classification, aggressive normalization can beat linguistically "proper" lemmatization in inflection-heavy languages. - **A small, domain-tailored lexicon as binary features.** Simple has-positive-adjective and has-negative-adjective flags drove most of the sentiment performance. A focused hand-built word list can be worth more than elaborate feature engineering. - **Checking features against the baseline.** Without the lexicon, the sentiment models stayed below the majority baseline. Always reporting that baseline exposes when a feature-rich setup is not actually learning the task. - **Separate test evaluation for the best configurations only.** Settings were chosen by cross-validation on 80% of the data, then only the winners were scored on a held-out 20%. That split is larger than any single dev fold, which limits overfitting to the development data. *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 - [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 241 works · 54,053 citations - [Indiana University Bloomington](https://md.research.elamur.ai/free/en/universities/indiana-university-bloomington/) — University, Bloomington, United States · 37 works · 3,685 citations 📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 1,552 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 870 works 🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 972 works · [Semitic languages](https://md.research.elamur.ai/free/en/topics/semitic-languages/) · 2,908 works ## Cite Cite as: Muhammad Abdul-Mageed, Mona Diab and Mohammed Korayem. 2011. Subjectivity and Sentiment Analysis of Modern Standard Arabic. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{abdulmageed2011subjectivity, title = {{Subjectivity and Sentiment Analysis of Modern Standard Arabic}}, author = {Muhammad Abdul-Mageed, Mona Diab and Mohammed Korayem}, booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies}, year = {2011}, url = {https://aclanthology.org/P11-2103/} } ``` - [← previous Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments](https://md.research.elamur.ai/free/en/papers/mohler-2011-learning-grade-short-answer-questions/) - [next → Overview of BioNLP Shared Task 2011](https://md.research.elamur.ai/free/en/papers/kim-2011-overview-bionlp-shared-task-2011/) - [↑ back to papers of 2011](https://md.research.elamur.ai/free/en/papers/2011/) ## References 12 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. R. Bruce and J. Wiebe. 1999. Recognizing subjectivity. a case study of manual tagging. Natural Language Engineering, 5(2). [find](https://scholar.google.com/scholar?q=R%2E+Bruce+and+J%2E+Wiebe%2E+1999%2E+Recognizing+subjectivity%2E+a+case+study+of+manual+tagging%2E+Natural+Language+Engineering%2C+5%282%29%2E) 2. T. Joachims. 2008. Svmlight: Support vector machine. [http://svmlight.joachims.org/](http://svmlight.joachims.org/), [↗](http://svmlight.joachims.org/) 3. Cornell University, 2008. [find](https://scholar.google.com/scholar?q=Cornell+University%2C+2008%2E) 4. S. Kim and E. Hovy. 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. 5. M. Maamouri, A. Bies, T. Buckwalter, and W. Mekki. 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=M%2E+Maamouri%2C+A%2E+Bies%2C+T%2E+Buckwalter%2C+and+W%2E+Mekki%2E+2004%2E+The+penn+arabic+treebank%3A+Building+a+large%2Dscale+annotated+arabic+corpus%2E+In+NEMLAR+Conference+on+Arabic+Language+Resources+and+Tools%2C+pages+10) 6. R. Tsarfaty, D. Seddah, Y. Goldberg, S. Kuebler, Y. Versley, M. Candito, J. Foster, I. Rehbein, and L. Tounsi. 2010. [Statistical parsing of morphologically rich languages (spmrl) what, how and whither](https://md.research.elamur.ai/free/en/papers/tsarfaty-2010-statistical-parsing-morphologically-rich-languages/). In Proceedings of the NAACL HLT 2010 First Workshop on Statistical Parsing of Morphologically-Rich Languages, Los Angeles, CA. 7. J. Wiebe, R. Bruce, and T. O’Hara. 1999. [Development and use of a gold standard data set for subjectivity classifications](https://md.research.elamur.ai/free/en/papers/wiebe-1999-development-use-gold-standard-data/). In Proc. 37th Annual Meeting of the Assoc. for Computational Linguistics (ACL-99), pages 246– 253, University of Maryland: ACL. 8. J. Wiebe, T. Wilson, R. Bruce, M. Bell, and M. Martin. 2004. [Learning subjective language](https://md.research.elamur.ai/free/en/papers/wiebe-2004-learning-subjective-language/). Computational linguistics, 30(3):277–308. [↗](https://doi.org/10.1162/0891201041850885) 9. J. Wiebe. 1994. [Tracking point of view in narrative](https://md.research.elamur.ai/free/en/papers/wiebe-1994-tracking-point-view-narrative/). Computional Linguistics, 20(2):233–287. [↗](https://doi.org/10.5555/972525.972529) 10. T. Wilson, J. Wiebe, and P. Hoffmann. 2009. [Recognizing Contextual Polarity: an exploration of features for phrase-level sentiment analysis](https://md.research.elamur.ai/free/en/papers/wilson-2009-recognizing-contextual-polarity-exploration-features/). Computational Linguistics, 35(3):399–433. [↗](https://doi.org/10.1162/coli.08-012-r1-06-90) 11. J. Yi, T. Nasukawa, R. Bunescu, and W. Niblack. 2003. Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining, pages 427–434. [find](https://scholar.google.com/scholar?q=J%2E+Yi%2C+T%2E+Nasukawa%2C+R%2E+Bunescu%2C+and+W%2E+Niblack%2E+2003%2E+Sentiment+analyzer%3A+Extracting+sentiments+about+a+given+topic+using+natural+language+processing+techniques%2E+In+Proceedings+of+the+3rd+IEEE+Interna) 12. H. Yu and V. Hatzivassiloglou. 2003. The penn arabic treebank: Building a large-scale annotated arabic corpus. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 129– 136. [find](https://scholar.google.com/scholar?q=H%2E+Yu+and+V%2E+Hatzivassiloglou%2E+2003%2E+The+penn+arabic+treebank%3A+Building+a+large%2Dscale+annotated+arabic+corpus%2E+In+Proceedings+of+the+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pag) *Updated 1 November 2026*