The proliferation of blogs and social networks presents a new set of challenges and
opportunities in the way information is searched and retrieved. 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
presidential candidate they support. Thus, there is a need for a new type of search engine
which will not only retrieve facts, but will also enable the retrieval of opinions. Such a
search engine can be used in a number of diverse applications like product reviews to
aggregating opinions on a political candidate or issue. Enterprises can also use such an
engine to determine how users perceive their products and how they stand with respect
to competition. 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 is integrated
in an opinion search engine which presents results to a query along with their overall tone
and a summary of sentiments of the most important features.
