--- title: "Arabic Text Sentiment Analysis — Almurqren 2024" description: "The authors map research on Arabic sentiment analysis (ASA), a field with far less work than English sentiment analysis." authors: ["Latifah Almurqren", "Ryan Hodgson", "Alexandra Cristea"] year: 2024 venue: "arXiv:2403.01921" source: "https://arxiv.org/abs/2403.01921" doi: "10.48550/arxiv.2403.01921" arxiv: "2403.01921" licence: "CC BY 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/almurqren-2024-arabic-text-sentiment-analysis-reinforcing/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/almurqren-2024-arabic-text-sentiment-analysis-reinforcing/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) / Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis # Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis **Authors:** Latifah Almurqren, Ryan Hodgson, Alexandra Cristea · **Institutions:** [Durham University](https://md.research.elamur.ai/free/en/universities/durham-university/) · **Year:** 2024 · **Venue:** arXiv:2403.01921 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2403.01921) · [PDF](https://arxiv.org/pdf/2403.01921) · [DOI 10.48550/arxiv.2403.01921](https://doi.org/10.48550/arxiv.2403.01921) · [arXiv 2403.01921](https://arxiv.org/abs/2403.01921) · [OpenAlex](https://openalex.org/W4392488718) **Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 140884 papers whose bibliographies we read ## Summary The authors map research on Arabic sentiment analysis (ASA), a field with far less work than English sentiment analysis. Arabic is hard for this task: it has rich morphology, often drops diacritics, and mixes Modern Standard Arabic (MSA) with many regional dialects. The review has two layers. First, a manual, PRISMA-style systematic review of 133 English-language ASA papers from 2002–2020, drawn from SAGE, IEEE, Springer, WILEY and Google Scholar. Second, an automatic analysis of 2297 open-access ASA publications from 2010–2020, using topic modelling and temporal trends to test whether the manual themes hold at scale. The main findings: - SVM, Naive Bayes and hybrid methods are the most common strong performers. - Deep models such as LSTM can be more accurate but are limited by small corpora. - Lexicons with wide coverage are MSA-only, and dialect lexicons are small. - Tweet corpora are only mid-sized, and practical ASA tools for industry are scarce. The work gives a reference map of methods and resources and points to dialect data as the main gap. ## Key points - Screening funnel: 702 records found (234 from databases, 468 from grey literature), 15 duplicates removed, 554 excluded at title, abstract or method level, and 133 papers kept after full-text review. - The automatic in-breadth study covers 2297 publications. The best topic model used 201 topics with ɑ = 0.01 and reached coherence 0.39. The resulting topics were then hand-labelled into themes such as Transformer Networks, Convolutional Networks, Recurrent Networks, Bayesian Learning, Semantic Web, Twitter and Facebook. - The approach families identified are machine learning, lexicon-based and hybrid. Transfer learning was nearly absent: only one study in 2019 and one in 2020. - Lexicon resources vary widely in size: - ArSenL: 157,969 words - SLSA: 35,000 lemmas - NileULex: 5953 Egyptian dialect words and phrases - SauDiSenti: a Saudi dialect sentiment lexicon - smaller lexicons that map Jordanian dialect, Arabizi or emoticons onto MSA - Saudi tweet corpora range from about 1,331 to 17,573 tweets (AraSenti-Tweet), and most are not publicly available. Six dialect groups are distinguished: Gulf, Yemeni, Iraqi, Egyptian, Levantine and Maghrebi. ## Interesting ideas - The authors check a small, carefully hand-read sample against a large automatic analysis of thousands of documents, which turns the hand-read conclusions into claims with quantitative backing. — For Mova (learn): Mova's evaluation could pair a small, manually audited set of parses or frames with corpus-wide automatic statistics, flagging cases where the two views disagree as candidates for repair rules. - The topic model is deliberately run with very many fine topics, and humans then merge and name the useful ones, so the machine supplies clusters while people supply the labels. — For Mova (world): the same over-cluster-then-curate loop could help grow ontology categories or verb-class groupings from corpus co-occurrence, while keeping each final category explainable and named. - Frequent multiword units such as naive_bayes or twitter_feed are joined into single tokens before modelling, so they are not split into misleading separate words. — For Mova (tokens): detecting and lemmatizing multiword expressions as one unit before tagging would give later stages cleaner lemmas for names and fixed terms. - Hierarchical sentiment classification first decides whether a text is subjective or objective, and only then assigns polarity to the subjective part. — For Mova (frames): prag could first decide whether a clause is evaluative or factual before assigning stance or irony, which avoids forcing polarity onto plain statements. - Several of the surveyed lexicons translate dialect, Arabizi or emoticons into the standard variety, so existing standard-language resources can be reused for informal text. — For Mova (tree): a normalisation layer that maps informal or dialectal English onto standard forms before parsing would let the existing treebank-trained parsers cover social-media text. *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/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{almurqren2024arabic, title = {{Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis}}, author = {Almurqren, Latifah and Hodgson, Ryan and Cristea, Alexandra}, year = {2024}, doi = {10.48550/arxiv.2403.01921}, eprint = {2403.01921}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2403.01921} } ``` - [← previous An Old Uigur medical fragment in Syriac script](https://md.research.elamur.ai/free/en/papers/karoly-2024-old-uigur-medical-fragment-syriac/) - [next → Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter](https://md.research.elamur.ai/free/en/papers/alshehri-2024-arabic-tweet-act-weighted-ensemble/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/)