Keywords Extractor using NLP

Case Study

Problem: Manually identifying the most relevant keywords from large text documents is time-consuming and error-prone.Traditional keyword extraction methods often fail to capture context or semantic meaning accurately.There was a need to practice natural language processing (NLP) techniques to automate and improve keyword detection.The project aimed to build an educational prototype to explore core concepts in text preprocessing and feature extraction.

Approach: Built a lightweight application in Google Colab integrating Python and NLP libraries.Preprocessed text with tokenization, stop-word removal, stemming/lemmatization to clean input data.Applied statistical techniques such as TF-IDF and RAKE, along with ML-based methods, to extract high-value keywords.Developed a simple HTML interface to demonstrate keyword extraction on text-based files or user input.

Impact: Enabled quick identification of important keywords, reducing manual effort in text analysis.Improved understanding of NLP workflows including preprocessing, feature engineering, and evaluation.Provided a reusable foundation for applications such as document summarization, tagging, and search optimization.Served as a practical learning project bridging theoretical NLP concepts with real-world applications.