1. A computer implemented method for analyzing sentiment concerning a subject, comprising the steps of: collecting an object from an external content repository, wherein the collected objects form a content database; extracting snippets related to the subject from the content database; calculating a sentiment score for at least one snippet that has been extracted; for the at least one snippet for which the sentiment score has been calculated, classifying the snippet into an at least one sentiment category; creating sentiment taxonomy using the sentiment categories, wherein the sentiment taxonomy classifies the snippets as positive, negative or neutral; identifying topic words within the sentiment taxonomy; classifying the topic words as a sentiment topic word candidates or a non-sentiment topic word candidate, wherein the non-sentiment topic word candidates are filtered by using a stoplist and a list of sentiment words; identifying the frequency of the non-sentiment topic words in each of the sentiment categories; identifying the importance of the non-sentiment topic word for each of the sentiment categories; and, ranking the topic word, wherein the rank is calculated by combining the frequency of the topic words in each of the categories with its importance.