Extracting Keywords From Text Using NLP On Azure Virtual Machine

NLP methods (Natural Language Processing) are used in this project to approach fetching the all keywords by written content and are deployed on an Azure Virtual Machine (VM). Both texts summarization and information retrieval, keyword extraction is essential. The project seeks to create an accurate and efficient method for identifying and retrieving keywords from several sources text sources by leveraging the plus point of NLP libraries and tools. By utilizing Azure VM's scalability and computational power, the project provides reliable processing of significant amounts of text data. The project's conclusion provides a useful tool for improving document categorization, subject analysis, and content summarization, with the added benefit of utilizing cloud resources for top performance. As a way to determine distinctions between every single viewpoint of the individual by algorithms, we have considered numerous factors models and their implementation in this work. We initially learned how to convert PDF files to text before learning how to get keywords out of documents using a variety of algorithms, including RAKE YAKE TFIDF and modifying them in a way that ensures we receive the best-extracted keywords that relate to the document and aid in comprehending it. In the end, we evaluated various methods using mined keywords. Additionally, it gave us the ability to judge which algorithm was the most effective overall. Given the actual facts, we could make an educated judgment as to which method produces the best keywords.