A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study
2026-08-24 • Computation and Language
Computation and Language
AI summaryⓘ
The authors analyzed over 7,000 research papers on Arabic Natural Language Processing (NLP) from 1960 to 2026 to understand trends in the field. They found that research greatly increased after 2020, mainly due to new transformer models and large language models. Using topic modeling, they identified 19 main research areas, with a focus on text, speech, translation, and recognition. Their citation analysis showed older papers and those from certain institutions tend to be cited more. They also highlighted neglected dialects like Maghrebi, Iraqi, and Sudanese that need more study.
Natural Language ProcessingArabic NLPTransformer modelsLarge language modelsTopic modelingBibliometric analysisCitation analysisDialectal ArabicSocial network analysisBERTopic
Authors
Mullosharaf K. Arabov
Abstract
Natural Language Processing (NLP) has grown rapidly over the past decade, driven by digital transformation in the Arab world, social media, and large language models (LLMs). Despite this growth, a comprehensive quantitative meta-analysis of the field remains absent. This study presents a large-scale bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from six collections. We employ BERTopic for topic modeling, regression analysis to identify citation predictors, social network analysis for co-authorship structures, and geographic mapping. Our findings show a significant publication surge after 2020, driven by transformer models and LLMs. Topic modeling identifies 19 substantive themes, the largest centered on text, speech, translation, and recognition. Citation analysis reveals a positive correlation between paper age and citations (r = 0.245, p < 0.001); regression shows that indexing in OpenAlex or Semantic Scholar and institutional affiliation are associated with higher citation counts. Saudi Arabia, the United States, and Egypt lead in research output. A task-dialect gap matrix identifies critical understudied areas, including summarization for Maghrebi, Iraqi, and Sudanese dialects. The largest topic has the highest H-index (87), followed by sentiment analysis (54). Our quantitative approach complements existing qualitative surveys and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks for Arabic NLP.