Abstract :

In The Digital Age, A Massive Amount Of Text Data Is Continuously Generated Through Social Media Platforms, News Feeds, Blogs, And Online Discussions. Identifying Hot Topics And Emerging Trends From Such Streaming Text Data Is Crucial For Understanding User Opinions, Predicting Market Behavior, And Detecting Real-time Events. This Project Aims To Design A System That Processes Live Text Streams To Automatically Extract And Analyze Trending Topics Using Natural Language Processing (NLP) And Machine Learning Techniques. The System Collects And Preprocesses Textual Data, Performs Keyword Extraction, Clustering, And Sentiment Analysis To Determine Topic Relevance And Popularity Over Time. Algorithms Such As TF-IDF, Latent Dirichlet Allocation (LDA), And Real-time Data Analytics Are Utilized To Identify Evolving Discussions Effectively. The Proposed Model Provides Timely Insights That Can Support Decision-making In Fields Like Marketing, Social Media Monitoring, And News Analysis. Overall, It Offers A Scalable And Intelligent Solution For Detecting Patterns And Trends In Dynamic Text Streams. Keywords: Streaming Text Data, Hot Topic Detection, Natural Language Processing (NLP), Machine Learning, Real-Time Analytics, Trend Analysis, Topic Modeling.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

181-185

Section:

Articles

License:

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

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