Automated Research Paper Analysis Using Natural Language Processing In Python

Abstract

The rapid growth of scientific publications has created challenges in efficiently analyzing and reviewing research papers. This study presents an automated system that leverages Natural Language Processing (NLP) techniques in Python to perform comprehensive research paper analysis. The proposed model extracts key information such as title, abstract, keywords, and citation context to evaluate the relevance, novelty, and thematic alignment of a given paper. Using advanced NLP libraries like spaCy, NLTK, and transformer-based models (e.g., BERT), the system conducts text preprocessing, keyword extraction, semantic similarity assessment, and sentiment-based quality estimation. The framework supports reviewer assignment and paper categorization through embedding-based similarity scoring. Experimental evaluation demonstrates that the approach enhances the accuracy, speed, and consistency of research paper assessment compared to manual review. The system aims to serve as an intelligent assistant for editors, reviewers, and researchers, promoting data-driven decision-making in academic publishing.