  Online Reputation Monitoring (ORM) is concerned with the use of computational
tools to measure the reputation of entities online, such as politicians or
companies. In practice, current ORM methods are constrained to the generation
of data analytics reports, which aggregate statistics of popularity and
sentiment on social media. We argue that this format is too restrictive as end
users often like to have the flexibility to search for entity-centric
information that is not available in predefined charts. As such, we propose the
inclusion of entity retrieval capabilities as a first step towards the
extension of current ORM capabilities. However, an entity's reputation is also
influenced by the entity's relationships with other entities. Therefore, we
address the problem of Entity-Relationship (E-R) retrieval in which the goal is
to search for multiple connected entities. This is a challenging problem which
traditional entity search systems cannot cope with. Besides E-R retrieval we
also believe ORM would benefit of text-based entity-centric prediction
capabilities, such as predicting entity popularity on social media based on
news events or the outcome of political surveys. However, none of these tasks
can provide useful results if there is no effective entity disambiguation and
sentiment analysis tailored to the context of ORM. Consequently, this thesis
address two computational problems in Online Reputation Monitoring: Entity
Retrieval and Text Mining. We researched and developed methods to extract,
retrieve and predict entity-centric information spread across the Web.
