Other approaches rely on approximate heuristics for automatically gathering event phrases such as the presence of dates or similarity to existing rule-sets. Event processing in financial text has historically been largely knowledge- or pattern-based, relying on manually created rules for matching phrases to events. We defined economic events as prototypical schemata in which words expressing an event of a certain type (e.g., product releases, revenue increases, security value movements, deals) are linked to the participating persons, companies, and entities that play a role in the event (e.g., a product, the amount of increase in stock price, the main companies involved in a deal). Subsequently, we validated our novel resource with machine learning experiments in which we apply state-of-the-art deep learning models to check the feasibility of our task. In this dissertation, we present the construction of an extensive dataset for fine-grained event extraction and sentiment analysis in economic news, named SENTiVENT. The processing of subjective opinions, on the other hand, is performed with sentiment analysis systems, where positive or negative attitudes are detected towards products, persons, or organizations. Event extraction, on the one hand, is the task of automatically collecting the factual `who, what, where, when, why and how' of recent occurrences in news or social media. To capture the vast knowledge expressed in written language, the field of Information Extraction within Natural Language Processing aims to obtain structured information on facts and opinions from unstructured text.