This paper describes our opinion retrieval system for TREC 2008 blog track. We focused on five different aspects of the system. The first module is focussed on extracting the blog content out from junk html and thereby decreasing the noise in the indexed content. The second module aims at removing various kind of spam content from real blogs. The third module aimed at retrieving the relevant documents. The fourth module filters out opinionated documents and the fifth one calculated the polarity of the sentiments in the document. The final ranked retrieval runs were based on various combination of settings in each module so as to study the effect of each. For classification of subjectivity and polarity, the predictions we done using a complementary naive bayes classifier