Comments-Oriented Query Expansion for Opinion Retrieval in Blogs

Abstract

In recent years, Pseudo Relevance Feedback techniques have become one of the most effective query expansion approaches for document retrieval. Particularly, Relevance-Based Language Models have been applied in several domains as an effective and efficient way to enhance topic retrieval. Recently, some extensions to the original RM methods have been proposed to apply query expansion in other scenarios, such as opinion retrieval. Such approaches rely on mixture models that combine the query expansion provided by Relevance Models with opinionated terms obtained from external resources (e.g., opinion lexicons). However, these methods ignore the structural aspects of a document, which are valuable to extract topic-dependent opinion expressions. For instance, the sentiments conveyed in blogs are often located in specific parts of the blog posts and its comments. We argue here that the comments are a good guidance to find on-topic opinion terms that help to move the query towards burning aspects of the topic. We study the role of the different parts of a blog document to enhance blog opinion retrieval through query expansion. The proposed method does not require external resources or additional knowledge and our experiments show that this is a promising and simple way to make a more accurate ranking of blog posts in terms of their sentiment towards the query topic. Our approach compares well with other opinion finding methods, obtaining high precision performance without harming mean average precision.