4. The computer-implemented method of claim 3 , further comprising: creating from the categories of the electronic text a plurality of category/term of interest statistics of importance; and determining from the electronic text within each category and the category/term of interest statistics the importance of each co-occurrence.
4. The computer-implemented method of claim 3 , further comprising: creating from the categories of the electronic text a plurality of category/term of interest statistics of importance; and determining from the electronic text within each category and the category/term of interest statistics the importance of each co-occurrence. 5. The computer-implemented method of claim 4 , wherein the text clustering is configured to use a method based on selecting a cohesive terms of the electronic text to seed category selection, and wherein the at least one term having a sentiment score is categorized by: identifying all terms having a positive sentiment score and summing all positive sentiment scores; identifying all terms having a negative sentiment score and summing all negative sentiment scores; and calculating a difference between the sum of the positive sentiment scores and the sum of the negative sentiment scores.