Social Media based Emergency Response System

News is useful in a situation of disaster for communication, announcement, and request for rescue and so on. Alsoit causes a negative by-product which is spreading rumors. This paper describes how rumors has spread after natural or manmade disasters and emergency situations, and discusses how we can deal with them. We first investigated actual in-stances of rumors after the disaster. And then we attempted to disclose characteristics of that rumors. Based on the investigation we developed a system which detects candidates of rumors from news and then evaluated it. The result of experiment shows the proposed algorithm can and rumors with acceptable accuracy. Sentiment analysis deals with identifying and classifying opinions &sentiments expressed in thesource text. Social media generates vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated useful data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult as compared to general sentiment analysis due to presence of slang words and misspellings. Knowledge based approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. Public and private opinion about a wide variety of subjects are expressed and spread continually through numerous social media. Twitter is one of the social mediaplatforms that is gaining popularity. Twitter offers various organizations a fast and effective way to analyze customers perspectives toward the critical to success in the market place. Developing program for sentiment analysis is an approach to be used to computationally measure customers perceptions. This project uses knowledge base including various patterns for tweets along with the multiple strategies to detect the sentiment expressed in a tweet and if a tweet is genuine or not. Various machine learning and knowledge based approaches are used to compare patterns and apply strategies and NLP for sentiment analysis.