AnaliTexGra: A novel visual application for academic collaboration prediction based on standard machine learning techniques and text mining

AnaliTexGra: A novel visual application for academic collaboration prediction based on standard machine learning techniques and text mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AnaliTexGra: A novel visual application for academic collaboration prediction based on standard machine learning techniques and text mining Maria del Pilar Angeles, Francisco Barrios-Lopez, Jessica Zepeda Baeza, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7084709/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Large universities comprising multiple faculties and research institutes often face challenges in fostering collaboration among academics, primarily due to a lack of awareness regarding the research interests and publications of colleagues across different departments. Consequently, a system that facilitates the discovery of related or complementary research lines and scholarly outputs is needed, enabling more informed decisions for future interdisciplinary projects and enhancing overall academic productivity.This work presents a visual application for link prediction and text mining, designed with a strong focus on usability and deployed in a production-ready environment. It is intended to be easily accessible to the academic community, with the primary goal of fostering collaboration among researchers and supporting greater research productivity, rather than pushing the boundaries of the state of the art.This document presents the design, development, and functionality of a Web application that performs descriptive queries and predicts collaborations between academics. The approach can be generalized to other types of social networks. Various prediction models were generated and tested using statistical link heuristics with six numbers of common neighbors (NCN) and eight Non-NCN methods to train eight Supervised Classification models. The graph design, application design, feature engineering, model validation, and the results obtained are presented. We have successfully developed and deployed a password-protected application designed exclusively for use by researchers at our university. The system fulfills all objectives specified in the initial requirements, including the ability to load both custom datasets and the application's default database. It enables users to consult information on articles and authors through both graphical visualizations and textual queries. Unlike existing platforms, this application enables the identification of isolated research groups that may otherwise remain disconnected, providing university leadership, such as department heads, directors, or rectors, with actionable insights to foster integration and collaboration among scholars. link prediction supervised learning text mining web aplication Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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