TEXT ANALYZER USING MACHINE LEARNING

Abstract

With the growing volume of textual data generated daily from social media, emails, articles, and online platforms, extracting meaningful insights has become a significant challenge. The Text Analyzer using Machine Learning is a system designed to automatically process, classify, and extract valuable information from large text corpora. By leveraging machine learning algorithms, the system can perform tasks such as sentiment analysis, topic classification, keyword extraction, and content summarization, enabling users to understand and interpret text efficiently. The system preprocesses textual data through techniques like tokenization, stop-word removal, and vectorization to convert unstructured text into structured numerical representations. Machine learning models such as Support Vector Machines (SVM), Random Forest, Naïve Bayes, and Neural Networks are then applied to classify and analyze the text based on user-defined objectives. This allows for automated identification of patterns, trends, and sentiments in large datasets. By integrating predictive analytics and intelligent classification, the Text Analyzer enhances decision-making, supports content management, and provides actionable insights across various domains such as marketing, education, and social media monitoring. The system demonstrates high accuracy, scalability, and efficiency, making it a robust solution for understanding and managing textual information in real time.