Now a day’s sentiment analysis performs a very vital role in text mining. In essence web mining is a very broad area in a data mining field for extracts the sentiment of the text. To identify the sentiment of the textual data is a very challenging task. The present work focuses on sentence level negation identification and calculation from the News articles and Blogs. Two step approaches generally used for analysis namely preprocessing and post processing. Preprocessing consists of the tasks like stop word removing, punctuation mark removal, number removal, white space removal etc. Post processing comprises identification of sentiments from the text and calculation of score. The work analyses the performance of support vector machine, Naïve Bayes for the dataset collected online.

Sentiment Analysis, Support Vector Machine, Naïve Bayes, Machine Learning Algorithm

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