Open AccessQuantitative and Qualitative Approach of Scientific Paper Popularity By Naïve Bayes Classifier
Author(s) -
Tobias Ribeiro Sombra,
Rose Marie Santini,
Emerson Cordeiro Morais,
Walmir Oliveira Couto,
Alex de Jesus Zissou,
Pedro Silvestre da Silva Campos,
Portugal Santos,
Glauber Tadaiesky Marques,
Otávio André Chase,
José Felipe Souza de Almeida
Publication year - 2020
Language(s) - English
Resource type - Journals
ISSN - 2411-2933
DOI - 10.31686/ijier.vol8.iss8.2482
Subject(s) - popularity , naive bayes classifier , upload , social media , computer science , classifier (uml) , scientific literature , data science , world wide web , information retrieval , psychology , artificial intelligence , social psychology , support vector machine , biology , paleontology
Usually, scientific research begins with the collection of data in which online social media tools can be some of the most rewarding and informative resources. The extensive measure of accessible information pulls in users from undergraduate students to postdoc. The search for scientific themes has popularized due to the availability of abundant publications that resides in scientific social networks such as Mendeley, ResearchGate etc. Articles are published on these media inform of text for knowledge dissemination, scientific support, research, updates etc, and are frequently uploaded after its publication in a proceedings or journal. In this sense, data collected from database often contains high noise and its analysis can be treated as a characterization undertaking as it groups the introduction of a content into either good or bad. In this text, we present quantitative and qualitative analysis of papers popularity in Mendeley repository by using naive Bayes Classifier.