Investigating Learning Approaches for Blog Post Opinion Retrieval

Blog post opinion retrieval is the problem of identifying posts which express an opinion about a particular topic. Usually the problem is solved using a 3 step process in which relevant posts are first retrieved, then opinion scores are generated for each document, and finally the opinion and relevance scores are combined to produce a single ranking. In this paper, we study the effectiveness of classification and rank learning techniques for solving the blog post opinion retrieval problem. We have chosen not to rely on external lexicons of opinionated terms, but investigate to what extent the list of opinionated terms can be mined from the same corpus of relevance/opionion assessments that are used to train the retrieval system. We compare popular feature selection methods such as the weighted log likelihood ratio and mutual information for use both in selecting terms for training an opinionated document classifier and also as term weights for generating simpler (not learning base...

• Type: Conference
• Year: 2009
• Where: ECIR
• Authors: Shima Gerani, Mark James Carman, Fabio Crestani
• Keywords: Blog Post Opinion, Computer Science, ECIR 2009, Opinion Retrieval Problem, Post Opinion Retrieval