Learning Ranked Sentiment Lexicons

In contrast to classic retrieval, where users search factual information, opinion retrieval deals with the search of subjective information. A major challenge in opinion retrieval is the informal style of writing and the use of domain-specific jargon to describe the opinion targets. 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. The model is learned using online Variational Inference, a recently published method that can learn from streaming data and can scale to very large datasets. Opinion retrieval and classification experiments on two large datasets with 703,000 movie reviews and 189,000 hotel reviews showed that the proposed method outperforms the baselines while using a significantly lower dimensional lexicon than other methods.