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 analysis services. 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.