Opinions are used to express views and reviews are used to provide information about how a product is perceived. People contributions lie in posting text messages in the form their opinions and emotions which may be based on different topics such as movie, book, product, and politics and so on. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. The reviews are collected which periodically updates itself using crawler discussed in our previous work. Further after applying certain pre-processing tasks in order to filter reviews and remove unwanted tokens, the sentiments are classified according to the novel unsupervised algorithm proposed. Our algorithm does not require annotated training data and is adequate to sufficiently classify the raw text into each domain and it is applicable enough to categorize complex cases of reviews as well. Therefore, we propose a novel unsupervised algorithm for categorizing sentiments into positive, negative and neutral category. The accuracy of the designed algorithm is evaluated using the standard datasets like IRIS, MTCARS, and HAR.