|dc.description.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.||en_US