1. A method, in an information handling system comprising a processor and a memory, for analyzing concept vectors over time to detect changes in a corpus, the method comprising: generating, by the system, at least a first concept vector set V 1 ,. .. , Vk derived from a first set of concept sequences over k concepts that are extracted from the corpus and applied to a vector learning component; generating, by the system, at least a second concept vector set V′ 1 ,. .. , V′k+b derived from a concatenation of the first set of concept sequences and a second set of concept sequences over k old and b new concepts that are extracted from the corpus and applied to the vector learning component, where the second set of concept sequences is effectively collected after collection of the first set of concept sequences; and performing, by the system, a natural language processing (NLP) analysis of the first concept vector set and second concept vector set to detect changes in the corpus over time by detecting an appearance of one or more new concepts in the second set of concept sequences that are not present in the first set of concept sequences to identify market trends for answering questions submitted to the information handling machine by identifying vector changes for one or more concepts included in the first and/or second set of concept sequences, wherein detecting the appearance of one or more new concepts comprises: computing, by the system, a first cosine distance between each vector pair V′i, V′j from the second concept vector set V′ 1 ,. .. V′k, V′k+1,. .. , V′k+b for 1<i<k and k<j≦k+b; and identifying new concept pairs from the second set of concept sequences over k old and b new concepts having a strong interrelationship with concepts in the first set of concept sequences by reporting each concept pair Vi, Vj whereby the first cosine distance exceeds a first specified reporting threshold.