1. A method of text analysis comprising: applying a natural language processing technique for recognizing an ontological concept, by a concept recognition module, the recognizing comprising: receiving a stream of text comprising a plurality of entities; extracting at least one concept from said plurality of entities; disambiguating said at least one extracted concept, by a concept disambiguation module, comprising: receiving said at least one extracted concept; generating at least one disambiguated concept corresponding to said at least one extracted concept, wherein generating the at least one disambiguated concept comprises computing a confidence score signifying a likelihood of correctness of the at least one extracted concept, and further wherein the computing a confidence score comprises incorporating a comparison with each of: a plurality of positive example concepts and a plurality of negative example concepts, wherein the disambiguating the at least one extracted concept comprises comparing the at least one extracted concept with each of: the plurality of positive example concepts, and the plurality of negative example concepts; capturing user feedback, comprising: receiving said at least one disambiguated concept; validating said disambiguated concept with a first manual input received from a user; generating at least one of: a retained concept and a discarded concept; and updating the concept disambiguation module based on the at least one disambiguated concept and a second input from the user.
1. A method of text analysis comprising: applying a natural language processing technique for recognizing an ontological concept, by a concept recognition module, the recognizing comprising: receiving a stream of text comprising a plurality of entities; extracting at least one concept from said plurality of entities; disambiguating said at least one extracted concept, by a concept disambiguation module, comprising: receiving said at least one extracted concept; generating at least one disambiguated concept corresponding to said at least one extracted concept, wherein generating the at least one disambiguated concept comprises computing a confidence score signifying a likelihood of correctness of the at least one extracted concept, and further wherein the computing a confidence score comprises incorporating a comparison with each of: a plurality of positive example concepts and a plurality of negative example concepts, wherein the disambiguating the at least one extracted concept comprises comparing the at least one extracted concept with each of: the plurality of positive example concepts, and the plurality of negative example concepts; capturing user feedback, comprising: receiving said at least one disambiguated concept; validating said disambiguated concept with a first manual input received from a user; generating at least one of: a retained concept and a discarded concept; and updating the concept disambiguation module based on the at least one disambiguated concept and a second input from the user. 4. The method of claim 1 , wherein said recognizing the ontological concept further comprises: processing said plurality of entities by applying the natural language processing technique to generate said at least one extracted concept; generating feature vector representation corresponding to at least one of the plurality of entities linked to said at least one extracted concept in terms of a plurality of features associated with said extracted concept; and associating said vector representation with at least one of: said plurality of positive example concepts and said plurality of negative example concepts.