A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study

• URL: http://arxiv.org/abs/2608.23421v2
• Date: Tue, 25 Aug 2026 07:42:44 GMT
• Title: A Comprehensive Analysis of Arabic Natural Language Processing Research: Trends, Topic Evolution, and Research Gaps -- A Bibliometric and Topic-Based Study
• Abstract summary: This study presents a bibliometric and topic-based analysis of 7,120 Arabic NLP papers published between 1960 and 2026.<n>We employ BER for topic modeling, regression analysis, social network analysis, and geographic mapping.<n>Our findings show a significant publication surge after 2020, driven by transformer models and LLMs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Analyzing and Encoding the Al-Mawrid Arabic-English Dictionary with the ISO Language Markup Framework and TEI Lex-0 [1.1267872663780352]
  This paper presents a robust methodology for the systematic digitization and encoding of the Al-Mawrid Arabic-English dictionary.<n>It transforms it from a legacy print resource into a standardized computational lexicon.<n>By applying an editorial view to the dictionary's macro- and microstructure, the research resolves the structural ambiguities and inconsistencies typical of 20th-century bilingual lexicons.
  arXiv  Detail & Related papers  (2026-06-16T17:35:11Z)
• Paper Espresso: From Paper Overload to Research Insight [53.45135239997754]
  We present Paper Espresso, an open-source platform that automatically discovers, summarizes, and analyzes trending arXiv papers.<n>The system uses large language models (LLMs) to generate structured summaries with topical labels and keywords.<n>Over 35 months of continuous deployment, Paper Espresso has processed over 13,300 papers and publicly released all structured metadata.
  arXiv  Detail & Related papers  (2026-04-06T09:45:21Z)
• Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
  We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
  arXiv  Detail & Related papers  (2026-01-21T16:47:05Z)
• DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models [54.10223256792762]
  We present DialectalArabicMMLU, a new benchmark for evaluating the performance of large language models (LLMs) across Arabic dialects.<n>We extend the MMLU-Redux framework through manual translation and adaptation of 3K multiple-choice question-answer pairs into five major dialects.
  arXiv  Detail & Related papers  (2025-10-31T15:17:06Z)
• What fifty-one years of Linguistics and Artificial Intelligence research tell us about their correlation: A scientometric analysis [0.0]
  This study provides a thorough scientometric analysis of this correlation, synthesizing the intellectual production over 51 years, from 1974 to 2024.<n>The results indicate that in the 1980s and 1990s, linguistics and AI (AIL) research was not robust, characterized by unstable publication over time.<n>It concludes that linguistics and AI correlation is established at several levels, research centers, journals, and countries shaping AIL knowledge production and reshaping its future frontiers.
  arXiv  Detail & Related papers  (2024-11-29T17:12:06Z)
• Artificial intelligence contribution to translation industry: looking back and forward [0.0]
  This study provides a comprehensive analysis of artificial intelligence (AI) contribution to research in the translation industry (ACTI) over forty-five years from 1980-2024.<n>13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens; 9836 were unique records, which were used for the analysis.
  arXiv  Detail & Related papers  (2024-11-29T17:10:33Z)
• The Nature of NLP: Analyzing Contributions in NLP Papers [77.31665252336157]
  We propose a taxonomy of research contributions and introduce NLPContributions, a dataset of nearly $2k$ NLP research paper abstracts.<n>We show that NLP research has taken a winding path -- with the focus on language and human-centric studies being prominent in the 1970s and 80s, tapering off in the 1990s and 2000s, and starting to rise again since the late 2010s.<n>Our dataset and analyses offer a powerful lens for tracing research trends and offer potential for generating informed, data-driven literature surveys.
  arXiv  Detail & Related papers  (2024-09-29T01:29:28Z)
• Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
  We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
  arXiv  Detail & Related papers  (2024-06-27T19:26:11Z)
• SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature [97.31347312130119]
  SciRIFF (Scientific Resource for Instruction-Following and Finetuning) is a dataset of 137K instruction-following instances for training and evaluation, covering 54 tasks.<n>These tasks span five core scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.<n> SciRIFF is unique in being entirely expert-written, high-quality instruction-following dataset for extracting and synthesizing information from research literature across diverse scientific fields.
  arXiv  Detail & Related papers  (2024-06-10T21:22:08Z)
• Arabic Text Sentiment Analysis: Reinforcing Human-Performed Surveys with Wider Topic Analysis [49.1574468325115]
  The in-depth study manually analyses 133 ASA papers published in the English language between 2002 and 2020. The main findings show the different approaches used for ASA: machine learning, lexicon-based and hybrid approaches. There is a need to develop ASA tools that can be used in industry, as well as in academia, for Arabic text SA.
  arXiv  Detail & Related papers  (2024-03-04T10:37:48Z)
• ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic [51.922112625469836]
  We present datasetname, the first multi-task language understanding benchmark for the Arabic language. Our data comprises 40 tasks and 14,575 multiple-choice questions in Modern Standard Arabic (MSA) and is carefully constructed by collaborating with native speakers in the region. Our evaluations of 35 models reveal substantial room for improvement, particularly among the best open-source models.
  arXiv  Detail & Related papers  (2024-02-20T09:07:41Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)
• A bibliometric analysis of research based on the Roy Adaptation Model: a contribution to Nursing [0.0]
  To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s. We used information from the two dominant scientific databases, Web Of Science and SCOPUS.
  arXiv  Detail & Related papers  (2020-03-29T14:02:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.