International Journal of Innovative Research in Computer and Communication Engineering

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TITLE AI-Based News Analyzer and Prediction System
ABSTRACT The rapid proliferation of digital information has fundamentally altered the landscape of global news consumption. Millions of articles are published daily across heterogeneous online platforms, making the challenge of separating credible journalism from misinformation one of the most consequential problems of the modern information age. This paper presents the design, implementation, and evaluation of an AI-based News Analyzer and Prediction System that incorporates the K-Nearest Neighbours (KNN) algorithm as a central classification mechanism, alongside Logistic Regression, Naive Bayes, and Random Forest, for automated topic classification, sentiment analysis, fake news detection, and short-term trend prediction. KNN is a non-parametric, instance-based supervised learning algorithm that classifies a new data point by computing its distance from all training samples and taking a majority vote among the k closest neighbours. In the context of natural language processing, KNN operates on TF-IDF feature vectors derived from preprocessed news article text. The system achieves an overall classification accuracy of 80 to 87 percent across tasks, with KNN achieving 83 to 84 percent accuracy depending on the value of k, demonstrating competitive performance for practical news analysis deployments.
AUTHOR KAVYA M, KEERTHANA M, POORVIKA GURUVINA, NAYANA K S, PROF. ARCHANA K N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India
VOLUME 183
DOI DOI: 10.15680/IJIRCCE.2026.1404072
PDF pdf/72_AI-Based News Analyzer and Prediction System.pdf
KEYWORDS
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