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Now a days, huge amount data has generated on the internet and it is important to extract useful information from that huge data. Different data mining techniques are used to extract and implement to solve divers types of problems. In the era of News and blogs, there is need to extract news and need to analyze to determine opinion of that news reviews. Sentiment analysis finds an opinion i.e. positive or negative about particular subject. Negation is a very common morphological creation that affects polarity and therefore, needs to be taken into reflection in sentiment analysis. Automatic detection of negation from News article is a need for different types of text processing applications including Sentiment Analysis and Opinion Mining. Our system uses online news databases from one resources namely BBC news. While handling news articles, we executed three subtasks namely categorizing the objective, separation of good and bad news content from the articles and performed preprocessing of data is cleaned to get only what is required for analysis, Steps like tokenization, stop word removal etc. The currently work focuses on different computational methods modeling negation in sentiment analysis. Especially, on aspects level of representation used for sentiment analysis, negation word recognition and scope of negation and identification.