Research Paper Finder: A Web-based Application for Efficient Academic Literature Retrieval using Web Scraping

In the present world, fast growth in academic publications has made it difficult to search for researchers. It consumes great time in searching among many results from traditional search engines for relevant research papers. This study proposes a web-based application, "Research Paper Finder" which helps enhance the research process by availing the easy access of relevant academic papers. The application uses web-scraping techniques to acquire research papers from Google Scholar based on user-provided keywords. The application is developed in Python, Streamlit, and BeautifulSoup, and delivers paper titles, abstracts, and direct links that allow users to check papers without wasting much time. The proposed solution to the problem will improve literature review efficiency by reducing the time lost spent on manual searches. In general, it will give an advantage to researchers, students, and academicians in their efforts to find relevant scholarly information. The experimental results indicate that search accuracy and retrieval speed are enhanced by the system, thus contributing to the improved efficiency of a literature review process. Future work will include the extension of data sources and the provision of machine learning techniques for relevance ranking and personalized paper recommendations.