Ranking documents or sentences according to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polarities of the targeted text are not explicitly given, as is often the case on the web. In this paper, we propose several sentiment information retrieval models in the framework of probabilistic language models, assuming that a user both inputs query terms expressing a certain topic and also specifies a sentiment polarity of interest in some manner. We combine sentiment relevance models and topic relevance models with model parameters estimated from training data, considering the topic dependence of the sentiment. Our experiments prove that our models are effective.
Eguchi and Lavrenko REF proposed several sentiment information retrieval models in the framework of probabilistic language models, assuming that users are willing to specify the sentiment polarity of interest when they input query terms.
Sentiment Retrieval Using Generative Models
Conference On Empirical Methods In Natural Language Processing
Computer Science