Daily News Classification and Trend Analysis System Using Machine Learning

Abstract

The digital age has catalyzed an explosion in the volume and velocity of news content published every hour across hundreds of online platforms.Theinabilitytoautomaticallyorganize,categorize,and extractmeaningfulsignal sfromthistorrent of informationrepresentsa significantchallengeformediacompanies,policymakers,researchers, andthegeneral publicalike. Thispaperpresentsa comprehensive DailyNewsClassificationand Trend AnalysisSystemthatintegratesmultiple machinelearning (ML)algorithmswithNaturalLanguage Processing (NLP) techniques to build an end-to-end, scalable news intelligence pipeline. The system ingests raw news articles from heterogeneous sources, applies a rigorous preprocessing pipeline including tokenization, stop word removal, stemming, and lemmatization, andthenextractsdiscriminativefeaturesusingTF-IDFvectorizationwithn-gramextensions.Threeclassificationmodels—Multinomial Naive Bayes, Support Vector Machine (SVM) with linear kernel, and Logistic Regression — are trained and benchmarked on standard corpora.Thebest-performingclassifier(SVMwithTF-IDFbigrams)achieves94.1%accuracyontheAGNewscorpus.Adedicatedtrend analysis module applies moving averages, rate-of-change measures, and Kleinberg\'s burst detection algorithm to identify temporally emerging and declining topic categories. Real-world experiments over a six-month news archive confirm the system\'s ability to detect genuine trend patterns aligned with known world events. An interactive dashboard provides real-time visualization of category-leveltrends, enabling actionable intelligence for media monitoring, public opinion analysis, and misinformation detection pipelines. The system is modular, language-extensible, and deployable on standard commodity hardware.

Introduction

It explains how traditional manual or rule-based methods are insufficient for processing the huge volume of global news, so the system uses machine learning (TF-IDF features and classifiers like Naive Bayes, SVM, and Logistic Regression) to automatically categorize news into topics such as politics, sports, and technology. It then extends this with a trend analysis module that tracks how news categories change over time using moving averages, rate-of-change metrics, and burst detection techniques.

The system is built as an end-to-end pipeline, including data ingestion from APIs, preprocessing of raw text, feature extraction, classification, and visualization through a real-time dashboard. It is evaluated on standard datasets (AG News and BBC News), where the SVM model performs best with about 94% accuracy.

The key contribution is integrating both news classification and temporal trend detection into one system, rather than treating them separately. The study also highlights practical applications like media monitoring, event detection, and real-time news analytics, along with the system’s efficiency and suitability for real-world deployment.

Conclusion

This paper presented a comprehensive Daily News Classification and Trend Analysis System that addresses the growing challengeof making senseof the high-velocity digital news ecosystem. The system integrates a robust NLP preprocessing pipeline,TF-IDFfeatureextractionwithn-gramextensions,andthreesupervisedclassificationmodelsintoasinglecoherent architecture. Extensive experiments on two benchmark datasets (AG News and BBC News) demonstrated that the SVM classifier with TF-IDF bigrams and title/description weight boosting achieves 94.7% and 97.3% accuracy respectively — highly competitive with more complex deep learning approaches at a fraction of the computational cost. The trend analysis module, combining moving averages, rate-of-change analysis, and Kleinberg\'s burst detection in an ensemble voting scheme, identified 30 of 34 independentlyannotated trend events in a six-month real-world news archive with 91.4% precision and an average detection latency of 1.5 days. The system operates at approximately 900 articles per minuteoncommodityhardware,satisfyingthereal-timerequirementsofnewsmonitoringapplications.AninteractivePlotly Dash dashboard provides stakeholders with intuitive access to classification and trend data through time-series plots, heatmaps, and category-level word clouds. Theworkconfirmsthatwell-engineeredclassicalMLpipelinesremainhighlyeffectivefornewsclassificationinproduction settings, particularly where computational resources are constrained or low-latency inference is critical. The modular architecture ensures that individual components can be upgraded independently as better tools become available. Several promising directions for future work have been identified. First, integrating transformer-based classifiers such as DistilBERTorRoBERTa,potentiallyviamodeldistillationtoreduceinferencelatency,isexpectedtoyieldfurtheraccuracy gainsof2–4percentagepointsonhardermulti-classbenchmarks.Second,extendingthesystemtosupportmultilingualnews ingestion — particularly for Indian regional language news — would substantially broaden its social impact. Third, incorporatingasentimentanalysislayerinto thetrendmodulewouldenabledirection-awaretrenddetection,distinguishing between positive and negative coverage spikes. Fourth, a multi-label classification extension would better handle the pervasive category ambiguity in real-world news. Finally, an active learning feedback loop integrated into the dashboard would allow domain experts to correct misclassifications and continuously improve model performance without requiring periodic full retraining.

References

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Copyright

Copyright © 2026 Rathnaprasad D, Akhilesh B, Sarathy , Ms. M. Subashani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.