SABRE: A Sentiment Aspect-Based Retrieval Engine

The retrieval of pertaining information during the decision-making process requires more than the traditional concept of relevance to be fulfilled. This task asks for opinionated sources of information able to influence the user’s point of view about an entity or target. We propose SABRE, a Sentiment Aspect-Based Retrieval Engine, able to tackle this process through the retrieval of opinions about an entity at two different levels of granularity that we called aspect and sub-aspect. Such fine-grained opinion retrieval enables both an aspect-based sentiment classification of text fragments, and an aspect-based filtering during the navigational exploration of the retrieved documents. A preliminary evaluation on a manually created dataset shows the ability of the proposed method at better identify \(\langle \textit{aspect}, \textit{sub}\)-\(\textit{aspect}\rangle \) with respect to a term frequency baseline.