16. The computer program product of claim 13 , wherein the electronic text is web based.
13. A computer program product comprising a non-transitory computer useable storage medium to store a computer readable program, wherein the non-transitory computer readable program, when executed on a computer, causes the computer to perform operations comprising: receiving the electronic text from a plurality of sources; determining an at least one term of interest to be identified in the electronic text; determining an at least one term of interest to be identified in the electronic text; identifying a plurality of locations within the electronic text including the at least one term of interest; for each location within a plurality of locations, creating by a snippet from a text segment around the at least one term of interest at the location within the electronic text; creating multiple taxonomies for the at least one term of interest from the snippets, wherein the taxonomies include an at least one category, the at least one category including a sentiment based taxonomy; and determining associations between categories of a different taxonomies of the multiple taxonomies by determining: co-occurrences between the multiple taxonomies; and significance of co-occurrences between the multiple taxonomies; determining co-occurrences between a category of a single taxonomy and the at least one term of interest to determine significance of the at least one term of interest; sorting the at least one term of interest by a respective significance; and outputting the sorted at least one term of interest, wherein at least one of the taxonomies is a time based taxonomy that is based on the creation date of the electronic text, the time based taxonomy generated by: crawling sources of electronic text to extract the creation dates; attaching an extracted creation date to a respective snippet to generate a dated snippet; and organizing the dated snippets into chronologically contiguous categories, wherein the sentiment based taxonomy is determined by: creating a list of positive, negative and neutral terms indicative of different sentiments, respectively; determining the level of sentiment corresponding to the at least one term generated from a respective snippet based on an assigned value; normalizing the values to generate at least one term having a sentiment score corresponding thereto, the sentiment score including at least one of a positive sentiment score and a negative sentiment score; and sorting snippets of the electronic text based on a calculated sentiment score differential between the at least one positive sentiment score and the at least one negative sentiment score.