6. A computer program product comprising a non-transitory computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: identify a dictionary of frequently used terms in a text data set U, wherein identifying the dictionary comprises representing each document of U as a vector of weighted frequencies of the document features, the document features being words and phrases contained in the document, wherein the vector is normalized to have unit Euclidean norm; create a feature space that identifies the dictionary term occurrences in each document of U; apply a rule induction algorithm to the feature space over U to identify rules that classify documents into categories based on a subset V of U, wherein the rule induction algorithm utilizes an entropy function that favors splitting the set U into categories, and wherein the rule induction algorithm creates a separate category for the documents in V and also the documents in U that are not in V; use feature based antecedents of each rule to describe events; and display the events using positive and negative antecedents, wherein the subset V comprises documents of U that were written within a specific time period, and the subset V provides an indication of emerging trends in the set U of documents that occur at a higher frequency during the specific time period than outside the specific time period.
6. A computer program product comprising a non-transitory computer useable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: identify a dictionary of frequently used terms in a text data set U, wherein identifying the dictionary comprises representing each document of U as a vector of weighted frequencies of the document features, the document features being words and phrases contained in the document, wherein the vector is normalized to have unit Euclidean norm; create a feature space that identifies the dictionary term occurrences in each document of U; apply a rule induction algorithm to the feature space over U to identify rules that classify documents into categories based on a subset V of U, wherein the rule induction algorithm utilizes an entropy function that favors splitting the set U into categories, and wherein the rule induction algorithm creates a separate category for the documents in V and also the documents in U that are not in V; use feature based antecedents of each rule to describe events; and display the events using positive and negative antecedents, wherein the subset V comprises documents of U that were written within a specific time period, and the subset V provides an indication of emerging trends in the set U of documents that occur at a higher frequency during the specific time period than outside the specific time period. 9. The computer program product of claim 6 , wherein: the rule induction algorithm is based on a decision tree; and each event is described by labeling the event with the antecedents that occur on the path in the decision tree from the leaf node of the event to the root node.