Opinion-aware Information Management: Statistical Summarisation and Knowledge Representation of Opinions.

Abstract

Nowadays, an increasing amount of media platforms provide the users with opportunities for sharing their opinions about products, companies or people. In order to support users accessing opinion-based information, and to support engineers building systems that require opinion-aware reasoning, intelligent opinion-aware tools and techniques are needed. This thesis contributes methods and technology for opinion-aware information management from two different perspectives, namely document summarisation and knowledge representation. Document summarisation has been widely investigated as a mean to reduce information overload. This thesis focuses on statistical models for summarisation, with a particular attention to divergence-based models, within the context of opinions. Firstly, topic-based document summarisation is addressed, contributing a study on divergence-based document to summary similarity and the definition of a novel algorithm for summarisation based on sentence removal. Secondly, summarisation models are tailored to opinion-oriented content and shown to be useful also when exploited for different tasks such as sentiment classification. Thirdly, summarisation models are applied to knowledge-oriented data, in order to tackle tasks such as entity summarisation. The comprehensive task addressed is the knowledge-based opinion-aware summarisation of content (free text, facts). This thesis also contributes a broad discussion on knowledge representation of opinions. A thorough study on how to model opinions using traditional techniques, such as Entity-Relationship (ER) modelling, underlines that a high-level, opinion-aware layer of conceptual modelling is useful since it hides away implementation details. A conceptual and logical knowledge representation methodology for modelling opinions is hence proposed, with the purpose of guiding engineers towards the use of best practices during the development of sentiment analysis applications. Specifically, an extension of the traditional ER modelling and the definition of an automatic mapping procedure, to translate opinion-aware components of the conceptual model into a relational model, help achieving a clear separation between conceptual and logical modelling. The mapping procedure yields an automatic and replicable methodology to design applications which require opinion-aware reasoning.