We present a method to perform automatic tagging of contextual factors associated with mobile payments data. Users specify a short description about the contextual factors interesting to them. The proposed system characterizes these factors and generates the knowledge concepts, similar to [1,2], but with the help of corpus statistics. These knowledge concepts describe the factors in terms of multi-faceted information search. Secondly, given a query, the underlying retrieval system retrieves top k texts pertaining to user information needs. Then based on the similarity between each of the knowledge concepts and the best matching texts, the context matching score is computed. Then the ranked sequence of contextual tags are assigned to the each retrieved text. The experimental results show that the proposed approach characterizes the context from user specified factors and performs the contextual tagging of the retrieved texts in a better way.