Opinion Retrieval From Blogs

Abstract

Opinion retrieval is a document retrieval process, which requires documents to be retrieved and ranked according to their opinions about a query topic. A relevant document must satisfy two criteria: relevant to the query topic, and contains opinions about the query, no matter if they are positive or negative. In this paper, we describe an opinion retrieval algorithm. It has a traditional information retrieval (IR) component to find topic relevant documents from a document set, an opinion classification component to find documents having opinions from the results of the IR step, and a component to rank the documents based on their relevance to the query, and their degrees of having opinions about the query. We implemented the algorithm as a working system and tested it using TREC 2006 Blog Track data in automatic title-only runs. Our result showed 28% to 32% improvements in MAP score over the best automatic runs in this 2006 track. Our result is also 13% higher than a state-of-art opinion retrieval system, which is tested on the same data set.