1. A computer implemented method for analyzing sentiment concerning a subject, comprising: collecting a plurality of objects from at least one 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 at least one sentiment category; creating a 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 sentiment topic words or non-sentiment topic words; identifying the frequency of the non-sentiment topic words in each of the sentiment categories; identifying the importance of the non-sentiment topic words in each of the sentiment categories; and, ranking the non-sentiment topic words, wherein the rank for a non-sentiment topic word is calculated by combining the frequency of the non-sentiment topic word in each of the sentiment categories with the importance of the non-sentiment topic word.
1. A computer implemented method for analyzing sentiment concerning a subject, comprising: collecting a plurality of objects from at least one 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 at least one sentiment category; creating a 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 sentiment topic words or non-sentiment topic words; identifying the frequency of the non-sentiment topic words in each of the sentiment categories; identifying the importance of the non-sentiment topic words in each of the sentiment categories; and, ranking the non-sentiment topic words, wherein the rank for a non-sentiment topic word is calculated by combining the frequency of the non-sentiment topic word in each of the sentiment categories with the importance of the non-sentiment topic word. 3. The computer implemented method as in claim 1 wherein identifying the importance of the non-sentiment topic words in each of the sentiment categories comprises calculating for each non-sentiment topic word a Pointwise Mutual Information of the non-sentiment topic word.