Entity-Based Opinion Mining from Text and Multimedia

Social web analysis is all about the users who are actively engaged and generate content. This content is dynamic, reflecting the societal and sentimental fluctuations of the authors as well as the ever-changing use of language. Social networks are pools of a wide range of articulation methods, from simple ”Like” buttons to complete articles, their content representing the diversity of opinions of the public. User activities on social networking sites are often triggered by specific events and related entities (e.g. sports events, celebrations, crises, news articles) and topics (e.g. global warming, financial crisis, swine flu). With the rapidly growing volume of resources on the Web, archiving this material becomes an important challenge. The notion of community memories extends traditional Web archives with related data from a variety of sources. In order to include this information, a semantically-aware and socially-driven preservation model is a natural way to go: the exploitation of Web 2.0 and the wisdom of crowds can make web archiving a more selective and meaning-based process. The analysis of social media can help archivists select material for inclusion, while social media mining can enrich archives, moving towards structured preservation around semantic categories. In this paper, we focus on the challenges in the development of opinion mining tools from both textual and multimedia content. We focus on two very different domains: socially aware federated political archiving (realised by the national parliaments of Greece and Austria), and socially contextualized broadcaster web archiving (realised by two large multimedia broad-