Treffer: Al-Powered Keyword Extraction System Using NLP Techniques for Contextual Insights and Document Accessibility.

This study presents the development of an AI-powered keyword extraction system using Python, aimed at efficiently identifying significant keywords from unstructured text data. Leveraging advanced natural language processing (NLP) techniques, the system integrates multiple methodologies, including Term Frequency-Inverse Document Frequency (TF-IDF), TextRank, Latent Semantic Analysis (LSA), and Part-of-Speech (POS) tagging. The incorporation of POS tagging enhances the accuracy of keyword identification by focusing on essential parts of speech, such as nouns and verbs, thereby filtering out irrelevant terms and ensuring the extraction of contextually meaningful keywords. To enhance user experience, the system includes innovative features such as a mobile document transfer facility that allows users to transfer documents via QR code and an option to download the extracted keywords as a document or PDF. These functionalities are designed to streamline accessibility and usability for diverse user groups. Performance evaluation was conducted using a dataset of research articles, demonstrating the system's effectiveness in accurately reflecting document content through keyword extraction. The results highlight its potential applications across various domains, including academia, marketing, and data science. This project offers a robust, intuitive, and user-friendly solution to make complex text data more accessible and actionable, contributing to the growing demand for efficient keyword extraction tools. [ABSTRACT FROM AUTHOR]

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