The main goal of this thesis is to improve sentiment and emotion analysis of text in social media through a combination of natural language processing, linked data and social network analysis. To achieve this goal, we have divided our research into three parts. First, we developed a semantic vocabulary to describe emotions, emotion models and
emotion analysis activities. This vocabulary enables a linked data approach to emotion
analysis, including in the annotation and processing of resources (e.g., datasets and lexicons),
and the development of public semantic emotion analysis services. We also extended the Marl
vocabulary for opinions and sentiment to include concepts of sentiment analysis activities.
Secondly, we modeled the different components in a sentiment or emotion analysis service, as well as the requirements to create public and interoperable services that can be
composed to produce advanced analyses. The result is a framework to model and develop
modular services. We also developed a reference implementation of this framework, which
can be used by researchers and developers to create and publish new sentiment and emotion
Thirdly, we studied and formalized the concept of social context, which is the information
in a social network that accompanies a text message and can be used to improve the analysis
of said text. We also developed a taxonomy of approaches to sentiment analysis based
on how they gather social context and how they exploit it in the analysis. In addition
to characterizing social context, we investigated several models of sentiment analysis that
enrich social context through social network analysis.