  This paper presents a novel two-stage framework to extract opinionated
sentences from a given news article. In the first stage, Naive Bayes classifier
by utilizing the local features assigns a score to each sentence - the score
signifies the probability of the sentence to be opinionated. In the second
stage, we use this prior within the HITS (Hyperlink-Induced Topic Search)
schema to exploit the global structure of the article and relation between the
sentences. In the HITS schema, the opinionated sentences are treated as Hubs
and the facts around these opinions are treated as the Authorities. The
algorithm is implemented and evaluated against a set of manually marked data.
We show that using HITS significantly improves the precision over the baseline
Naive Bayes classifier. We also argue that the proposed method actually
discovers the underlying structure of the article, thus extracting various
opinions, grouped with supporting facts as well as other supporting opinions
from the article.
