2018/02/05 by Tirthankar Ghosal, Rajeev Verma, Ghosal, Tirthankar +7
Business, Management and Accounting · Computer Science · #Advanced Text Analysis Techniques #Big Data and Business Intelligence #Data Mining Algorithms and Applications #Digital Libraries (cs.DL) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1802.01403
openalex publication_date 2018/02/05 · openalex created_date 2022/10/04 · openalex updated_date 2026/07/28
This work is a preliminary exploratory study of how we could progress a step towards an AI assisted article classification sys- tem in academia. The proposed system aims to aid the journal editors in their decisions by pinpointing the potential weaknesses or strengths of a submitted manuscript. From a large collection of articles and corresponding author-editor interactions we explore the possible reasons that lead to a paper being not forwarded for review. Our investigation reveals that in most cases either it is be- cause the prospective manuscript is out of scope of the journal or the manuscript does not satisfy the minimum quality requirements to maintain the standard of the journal. We extract several features to quantify the quality of a paper and the degree of in-scope explor- ing keyword search, citation analysis, reputations of authors and affiliations, similarity with respect to accepted papers. With these features we train standard machine learning classifiers to develop a classification system. On a decent set of test data our approach yields promising results across 3 different journals. We believe that our approach is generic and could be adapted to other journals with appropriate adjustments.