Fake News Detection & Sentimental Analysis on Twitter Data

Abstract: The fast growth of fake news on social media platforms raises a lot of concerns and has become a challenging task that has an impact on people on a global scale. This fake news is intended to deceive and manipulate its readers, while its main aim was to create awareness among its readers. Due to the emerge of such misleading information researchers, governments, journalists and fact-checking volunteers are working as a team together to address this issue. The automatic fake news detection systems enable identification of deceptive news with low accuracy. Hence designing a Fake news detector that has accuracy and precession is the goal. Sentiment evaluation and opinion mining is the sector that looks and analyses people's opinions the sentiments attached to it and evaluated it, the attitudes and feelings it poses from the written language. It is one of the most active research areas of NLP and text mining in recent years. It's mainly in demand because opinions are central and the reason for all the activities we perform and decisions we take reasons. Sentimental analysis helps us determine how the persons wellbeing has changed over a period of time and how the decisions taken have evolved from the changes in the surroundings

Keywords: ML, logistic regression, stemming, fake news detection, sentimental analysis