Semantic disambiguation in a social information discovery system

Sentiment Analysis of microblog content calls for specific tools able to cope with the dynamic nature of information published in social networks, and the intrinsic complexity and ambiguity of human language. In this work we introduce a Word Sense Disambiguation (WSD) algorithm for polysemous word disambiguation which uses a dictionary-based approach to determine the most fitting meaning of a term, basing on nearby words in the sentence. The work is a part of a Business Intelligence system for the integration and discovery of social information from multiple social networks, namely Facebook and Twitter. In this work we also extend the number of sources taking into account LinkedIn, as it is typically used by professionals, and discussions thereof provide added benefits when a non-generic evaluation of the topic to be analyzed is required.