There is disclosed a computer-implemented cloud computing scoring system. In an embodiment, a parser receives unstructured sentiment data commenting on a scored service. The parser identifies in the unstructured sentiment data a service category of the scored service. The parser selects from the unstructured sentiment data text relating to the service category and matching one or more opinionative words and phrases listed in a keyword dictionary, thereby producing a structured comment associated with the service category. The structured comment is classified as positive or negative according to a list of exemplary sentiment data sets contained in a learning seed file. The exemplary sentiment data sets are manually assigned a positive or a negative polarity. The learning seed file is configured for enhancement by the ongoing addition of structured sentiment data, the structured sentiment data commenting on the scored service and having a polarity classification.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented cloud computing scoring system, comprising: a parser receiving unstructured sentiment data commenting on a scored service, the parser identifying in the unstructured sentiment data a service category of the scored service; a keyword dictionary, the parser selecting from the unstructured sentiment data text relating to the service category and matching one or more opinionative words and phrases listed in the keyword dictionary, thereby producing a structured comment associated with the service category; a learning seed file for classifying the structured comment as positive or negative according to a list of exemplary sentiment data sets contained in the learning seed file, the exemplary sentiment data sets being manually assigned a positive or a negative polarity; and where the learning seed file is configured to be enhanced by the ongoing addition of structured sentiment data, the structured sentiment data commenting on the scored service and having a polarity classification. 2. The cloud computing scoring system of claim 1, wherein: the manual assigning of a positive or negative polarity to the exemplary sentiment data sets is performed by an industry expert. 3. The cloud computing scoring system of claim 1, wherein: the structured sentiment data is from a crowd-sourcing application for scoring the scored service. 4. The cloud computing scoring system of claim 1, wherein: the unstructured sentiment data is from at least one of a mobile application, Twitter®, Facebook®, unstructured analytics data, and social networking feeds. 5. The cloud computing scoring system of claim 1, further comprising: the learning seed file associating the polarity classification with opinionative words and phrases of the structured sentiment data for adding to the learning seed file if the learning seed file is enhanced thereby. 6. The cloud computing scoring system of claim 1, further comprising: a classifier classifying the structured comment as positive or negative according the exemplary sentiment data sets in the learning seed file, the classifier thereby generating a classified sentiment result; a structured sentiment result comprising the service category and the polarity classification of the structured sentiment data; and a data processing component weighting the classified sentiment result and the structured sentiment result according to a relative influence of each, the weighted results being combined and normalized on a standard scale into a normalized score of the scored service. 7. The cloud computing scoring system of claim 6, further comprising: an analytics scoring block receiving structured analytics data quantifying the scored service, the analytics scoring block processing the structured analytics data to generate a structured analytics result in a format compatible with the sentiment results, wherein the data processing component produces the weighted results by weighting the following according to a relative influence of each: the classified sentiment result, the structured sentiment result, and the structured analytics result. 8. The cloud computing scoring system of claim 1, further comprising: a classifier classifying the structured comment as positive or negative according the exemplary sentiment data sets in the learning seed file, the classifier thereby generating a classified sentiment result; an analytics scoring block receiving structured analytics data quantifying the scored service, the analytics scoring block processing the structured analytics data to generate a structured analytics result in a format compatible with the classified sentiment result; and a data processing component weighting the classified sentiment result and the structured analytics result according to a relative influence of each, the weighted results being combined and normalized on a standard scale into a normalized score of the scored service. 9. A computer-implemented cloud computing scoring system, comprising: a data acquisition component gathering data reporting on a scored service in a service category, the data gathered from at least two of unstructured sentiment data, structured sentiment data, and structured analytics data; a data analysis component performing sentiment analysis on the sentiment data to generate a classified sentiment result from the unstructured sentiment data and a structured sentiment result from the structured sentiment data, the data analysis component manually scoring the structured analytics data to generate a structured analytics result; a data processing component weighting the structured analytics result, the classified sentiment result, and the structured sentiment result according to a relative influence of each, the weighted results being combined and normalized into a normalized score on a standard scale; and a data application component displaying the normalized score for the scored service within the service category. 10. The cloud computing scoring system of claim 9, further comprising: a learning seed file for classifying the unstructured sentiment data as positive or negative according to a list of exemplary sentiment data sets contained in the learning seed file, the exemplary sentiment data sets being manually assigned a positive or a negative polarity, the learning seed file thereby generating the classified sentiment result; and where the learning seed file is configured to be enhanced by the ongoing addition of the structured sentiment data, the structured sentiment data commenting on the scored service and having a polarity classification. 11. The cloud computing scoring system of claim 10, wherein: the manual assigning of a positive or negative polarity to the exemplary sentiment data sets is performed by an industry expert. 12. The cloud computing scoring system of claim 9, wherein: the structured sentiment data is from a crowd-sourcing application for scoring the scored service. 13. The cloud computing scoring system of claim 9, wherein: the unstructured sentiment data is from at least one of a mobile application, Twitter®, Facebook®, unstructured analytics data, and social networking feeds. 14. A computer-implemented cloud computing scoring method, comprising: parsing unstructured sentiment data commenting on a scored service, the parsing identifying a service category of the scored service; selecting from the unstructured sentiment data text that matches one or more opinionative words and phrases listed in a keyword dictionary, thereby producing structured comment associated with the service category; classifying, using a learning seed file, the structured comment as positive or negative according to a list of exemplary sentiment data sets contained in the learning seed file, the exemplary sentiment data sets being manually assigned a positive or a negative polarity, said classifying thereby generating a classified sentiment result; and configuring the learning seed file to be enhanced by the ongoing addition of structured sentiment data, the structured sentiment data commenting on the scored service and having a polarity classification. 15. The method of claim 14, wherein: the manual assigning of a positive or negative polarity to the exemplary sentiment data sets is performed by an industry expert. 16. The method of claim 14, wherein: the structured sentiment data is from a crowd-sourcing application for scoring the scored service. 17. The method of claim 14, wherein: the unstructured sentiment data is from at least one of a mobile application, Twitter®, Facebook®, unstructured analytics data, and social networking feeds. 18. The method of claim 14, further comprising: associating, using the learning seed file, the polarity classification with opinionative words and phrases of the structured sentiment data for adding to the learning seed file if the learning seed file is enhanced thereby. 19. The method of claim 14, further comprising: generating a structured sentiment result comprising the service category and the polarity classification of the structured sentiment data; and weighting the classified sentiment result and the structured sentiment result according to a relative influence of each; combining said weighted sentiment results; and normalizing, on a standard scale, said combined weighted sentiment result into a normalized score of the scored service. 20. The method of claim 19, further comprising: receiving structured analytics data quantifying the scored service; processing the structured analytics data to generate a structured analytics result in a format compatible with the sentiment results; and wherein the following results are weighted according to a relative influence of each: the classified sentiment result, the structured sentiment result, and the structured analytics result.