NEWS WEB APPLICATION EVALUATION USING MACHINE LEARNING
DOI:
https://doi.org/10.29121/shodhkosh.v4.i2.2023.5726Keywords:
Graphical Representation, Flask Framework, Multiple Website Evaluation, Linear RegressorAbstract [English]
In our contemporary digital landscape, news consumption has transitioned largely to online platforms, shaping public opinion and influencing societal discourse. However, this shift has also led to the proliferation of misinformation and fake news, which can have profound consequences on public perception and decision-making processes. Moreover, the rise of social media has accelerated the dissemination of news, amplifying the impact of false information.
In response to these challenges, this project focuses on developing a robust framework for evaluating news websites using advanced techniques in Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML). The primary objective is to empower users with the ability to discern between credible and unreliable sources of information by performing binary classification of news articles.
Through the utilization of sophisticated algorithms and datasets, our system aims to analyse the content of online news articles and assess their authenticity. By leveraging AI and ML models, users will be equipped with tools to identify potentially misleading or fabricated news stories, thereby promoting critical thinking and informed decision-making.
Key features of the proposed system include the classification of news articles as either authentic or fake, as well as an evaluation of the credibility of the websites publishing the news. By harnessing the power of AI-driven analysis, this project endeavours to mitigate the spread of misinformation and enhance trust in online news sources.
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Copyright (c) 2023 Dr. Sunil Rathod, Dr. Vikas Nandgaoknar, Rutika Chougale
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