Abstract :

Text Feature Extraction Is A Fundamental Step In Natural Language Processing (NLP) That Converts Raw Textual Data Into Meaningful Numerical Representations For Machine Learning Models. With The Exponential Growth Of Unstructured Text Data From Social Media, Documents, And Web Content, Efficient Feature Extraction Techniques Are Essential For Tasks Such As Text Classification, Sentiment Analysis, And Information Retrieval. This Project Focuses On Implementing Various NLP Techniques To Extract Relevant Features From Text Data. The Proposed System Utilizes Preprocessing Methods Such As Tokenization, Stop-word Removal, Stemming, And Lemmatization To Clean And Normalize Textual Data. Feature Extraction Techniques Including Bag Of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), And Word Embeddings Are Applied To Transform Text Into Numerical Vectors. These Features Are Then Used To Train Machine Learning Models For Classification And Analysis Tasks. The System Is Implemented Using Python And NLP Libraries Such As NLTK And Scikit-learn. Experimental Results Demonstrate That Advanced Feature Extraction Techniques Improve Model Performance And Accuracy. This Approach Provides A Scalable And Efficient Solution For Processing Large Volumes Of Textual Data

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

2021-2026

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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