Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval

Abstract

In contrast to classic search where users look for factual information, opinion retrieval aims at finding and ranking subjective information. A major challenge of opinion retrieval is the informal nature of user reviews and the domain specific jargon used to describe the targeted item. In this paper, we present an automatic method to learn a space model for opinion retrieval. Our approach is a generative model that learns sentiment word distributions by embedding multi-level relevance judgments in the estimation of the model parameters. In addition to sentiment word distributions, we also infer domain specific named entities that due to their popularity become a sentiment reference in their domain (e.g. name of a movie, “Batman” or specific hotel items, “carpet”). This contrasts with previous approaches that learn a word’s polarity or aspect-based polarity. Opinion retrieval experiments were done in two large datasets with over 703.000 movie reviews and 189.000 hotel reviews. The proposed method achieved better, or equal, performance than the benchmark baselines.