Bibliometric and Scientometric analyses offer invaluable perspectives on the
complex research terrain and collaborative dynamics spanning diverse academic
disciplines. This paper presents pyBibX, a python library devised to conduct
comprehensive bibliometric and scientometric analyses on raw data files sourced
from Scopus, Web of Science, and PubMed, seamlessly integrating state of the
art AI capabilities into its core functionality. The library executes a
comprehensive EDA, presenting outcomes via visually appealing graphical
illustrations. Network capabilities have been deftly integrated, encompassing
Citation, Collaboration, and Similarity Analysis. Furthermore, the library
incorporates AI capabilities, including Embedding vectors, Topic Modeling, Text
Summarization, and other general Natural Language Processing tasks, employing
models such as Sentence-BERT, BerTopic, BERT, chatGPT, and PEGASUS. As a
demonstration, we have analyzed 184 documents associated with multiple-criteria
decision analysis published between 1984 and 2023. The EDA emphasized a growing
fascination with decision-making and fuzzy logic methodologies. Next, Network
Analysis further accentuated the significance of central authors and
intra-continental collaboration, identifying Canada and China as crucial
collaboration hubs. Finally, AI Analysis distinguished two primary topics and
chatGPT preeminence in Text Summarization. It also proved to be an
indispensable instrument for interpreting results, as our library enables
researchers to pose inquiries to chatGPT regarding bibliometric outcomes. Even
so, data homogeneity remains a daunting challenge due to database
inconsistencies. PyBibX is the first application integrating cutting-edge AI
capabilities for analyzing scientific publications, enabling researchers to
examine and interpret these outcomes more effectively.

本论文提出了一个名为 pyBibX 的 Python 库，用于从 Scopus、Web of Science 和 PubMed 等来源的原始数据文件中进行综合的计量和科学计量分析，并将最新的人工智能 (AI) 能力无缝集成到其核心功能中。该库执行了详细的探索性数据分析 (EDA)，并通过直观的图形说明呈现结果。PyBibX 是第一个整合最新 AI 能力用于分析科学出版物的应用程序，使研究人员能够更有效地检查和解释这些结果。