Natural language opinion search on blogs

Document Type

Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


In this paper, we present natural language opinion search by unifying discourse representation structures and the subjectivity of sentences to search for relevant opinionated documents. This technique differs from existing keyword-based opinion retrieval techniques which do not consider semantic relevance of opinionated documents at discourse level. We propose a simple message model that uses the attributes of the discourse representation structures and a list of opinion words. The model compute the relevance and opinionated scores of each sentence to a given query topic. We show that the message model is able to effectively identify which entity in a sentence is directly affected by the presence of opinion words. Thus, opinionated documents containing relevant topic discourse structures are retrieved based on the instances of opinion words that directly affect the key entities in relevant sentences. In terms of MAP, experimental results show that the technique retrieves opinionated documents with better results than the standard TREC Blog 08 best run, a non-proximity technique, and a state-of-the-art proximity-based technique. © 2012 Springer-Verlag.

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