FAKE NEWS DETECTION USING MACHINE LEARNIING

Abstract: Analysis of public information from social media could yield interesting results and insights into the globe of public opinions about almost any product, service or personality. Social network data is one amongst the foremost effective and accurate indicators of public sentiment. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of knowledge for opinions. Sentiment Analysis or Opinion Mining is that the computational treatment of opinions, sentiments and subjectivity of text. during this paper we are going to be discussing a strategy which allows utilization and interpretation of twitter data to see public opinions. Developing a program for sentiment analysis is an approach to be accustomed computationally measure customers' perceptions. This paper reports on the look of a sentiment analysis, extracting and training an unlimited amount of datasets. Results classify customers' perspective via datasets into positive and negative, which is represented during a chart, bar diagram, scatter plot using php, css and html pages.

Keywords: data processing, linguistic communication processing, Naïve Bayes.