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This study conducts a comprehensive bibliometric and topic-based analysis of 7,120 Arabic NLP papers published from 1960 to 2026, revealing significant trends and research gaps in the field. Utilizing methods such as BERTopic for topic modeling and regression analysis, the findings highlight a surge in publications post-2020, largely driven by advancements in transformer models and LLMs. Key insights include the identification of 19 research themes, a task-dialect gap matrix pointing to underexplored dialects, and a positive correlation between paper age and citation counts, emphasizing the need for focused research on less-studied areas.
A striking task-dialect gap reveals that critical areas like summarization for Maghrebi, Iraqi, and Sudanese dialects remain largely unaddressed in Arabic NLP research.
Arabic 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 remains absent. This study presents a bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026, sourced from five platforms (arXiv, ACL Anthology, Semantic Scholar, Crossref, OpenAlex) plus an additional targeted OpenAlex subset. We employ BERTopic for topic modeling, regression analysis, social network analysis, and geographic mapping. Our findings show a significant publication surge after 2020, driven by transformer models and LLMs. Topic modeling identifies 19 themes, the largest centered on text, speech, translation, and recognition (2,942 papers). Citation analysis reveals a positive correlation between paper age and citations (r = 0.245, p<0.001); regression (R^2 = 0.105) shows that indexing in OpenAlex or Semantic Scholar and institutional affiliation are associated with higher citations. Saudi Arabia, the United States, and Egypt lead in research output. A task-dialect gap matrix identifies understudied areas, including summarization for Maghrebi, Iraqi, and Sudanese dialects. The largest topic has the highest H-index (90), followed by sentiment analysis (57). Our quantitative approach complements existing qualitative surveys and offers recommendations to prioritize under-resourced dialects and develop culturally aligned benchmarks.