Research Paper Collection System Using Web Scrapping

This paper introduces an automated system designed to efficiently collect and retrieve research papers by integrating modern web scraping techniques, natural language processing (NLP), and advanced search technologies. The system employs BeautifulSoup to scrape and extract data from academic websites, ensuring comprehensive data collection. NLP techniques are then applied to analyze, categorize, and enhance the semantic relevance of the extracted information, thereby improving search accuracy. The processed data is indexed enabling fast and scalable retrieval with high relevance rankings. By automating these processes, the system significantly reduces the manual effort and time researchers typically invest in locating scholarly papers, thereby streamlining the research workflow.