Paper: Sentiment Retrieval Using Generative Models

ACL ID W06-1641
Title Sentiment Retrieval Using Generative Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006

Ranking documents or sentences accord- ing to both topic and sentiment relevance should serve a critical function in helping users when topics and sentiment polari- ties 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 frame- work of probabilistic language models, as- suming that a user both inputs query terms expressing a certain topic and also speci- fies 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.