Abstract: The Explosion of blogs, forums and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. This huge quantity of information on web platforms put together feasible for exercise as data sources, in applications based on opinion mining and classification. Interest in Opinion Mining has been growing steadily in the last years, mainly because of its great number of applications and the scientific challenge it poses. An effective sentiment analysis process proposes in this research for mining and classifying the opinions. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the product they support. Thus, there is a need for the retrieval of opinions. This paper presents an algorithm which not only analyzes the overall sentiment of a document/review, but also identifies the semantic orientation of specific components of the review that lead to a particular sentiment. The algorithm effectively optimizes the scores of the nouns to extract the potential features. The implementation is carried out on Customer Review Datasets and Additional Review Datasets and also the experimentation results are analyzed.
Keywords: Sentiment Analysis, Opinions.