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In this paper we present LanguageNet-a novel framework for processing unstructured text information from human generated content. The state of the art information processing frameworks have some shortcomings: modeled in generalized form, trained on fixed (limited) data sets, and leaving the specialization necessary for information consolidation to the end users. The proposed framework is the first major attempt to address these shortcomings. LanguageNet provides extended support of graphical methods contributing added value to the capabilities of information processing. We discuss the benefits of the framework and compare it with the available state of the art. We also describe how the framework improves the information gathering process and contribute towards building systems with better performance in the domain of Open Source Intelligence.