Real-time Sentiment Analysis by Extracting and Blocking of Abusing Tweets in Twitter using Predictive Machine Learning Algorithm

Twitter is an online social networking service. It enables users to send and read short 140-character messages called "tweets". Social networking is an easiest way to provide communication with people in different places and it has both advantages and disadvantages. Many people feel insecure to use Twitter as it shares all your tweets with third parties. As it is posted in public your privacy is in big jeopardy because of social media and it can affect your dignity and pride of a person. Twitter is one of the commonly used social media between short times it gained a worldwide popularity. More than 350 million of posts are tweeted per day. The major drawback of existing is that in twitter the tweets are publicly visible and the tweets can be visited by anyone via commented. So twitter does not provide privacy and security. The networking issues have become a matter of serious concern. The user feels insecure while using twitter as it shares all the personal information in common with third parties. In our proposed system we use Natural language processing (NLP) to identify malicious feedback ratings. NLP acts as a detecting technique which detects the negative or malicious tweets and also blocks the abused or negative comments by Sentiment Analysis and stopping it from sharing the personal details to the public blog.