Systems and methods for forecasting the prominence of various attributes in a future subject matter area are disclosed. An attribute is determined based on inputs received by a computing system. A set of indicators is determined based on the attribute and features extracted from an existing document set. The prominence of the attribute in the existing document set is determined. A prominence estimate of the attribute in a future document set is determined.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A prominence predictor system comprising one or more computing devices configured to: extract an attribute from input received by the one or more computing devices; determine a prediction type based on the attribute; select a plurality of indicators based on the prediction type, each indicator comprising a set of features extracted from an existing document set comprising documents (i) semantically related to the attribute and (ii) published over a time interval beginning prior to and ending on or before a current date; model the prominence of the attribute in accordance with the plurality of indicators in the existing document set over the time interval using one or more statistical modeling techniques; estimate future prominence of the attribute based on the model in a future document set that do not currently exist relative to prominence of the attribute in the existing document set; and interactively present content relating to the estimated future prominence of the attribute by one or more output devices of the one or more computing devices. 2. The system of claim 1, wherein: the attribute is associated with a feature extracted from at least one document of the existing document set, the attribute having a number of characteristics including an observed count of occurrences of the attribute in the existing document set, and the prediction type comprises a predicted count of occurrences of the attribute in the future document set based at least in part on the observed count of occurrences of the attribute in the existing document set. 3. The system of claim 1, wherein the set of indicators includes a subset of observable indicators having observed data extracted from the existing document set and a subset of derived indicators having derived data learned from the existing document set, the derived data being semantically determined from the observable data through the use of machine learning techniques. 4. The system of claim 1, wherein the one or more computing devices are further configured to determine a sentiment parameter for the attribute when the attribute is a term extracted from the documents of the existing document set, the sentiment parameter is indicative of whether an author of the term in the documents of the existing document set liked or disliked the attribute, the sentiment parameter being determined by analyzing each sentence containing the term in the existing set of documents. 5. The system of claim 1, wherein indicators of the set of indicators include static features having a single nominal value and dynamic features having data that changes as a function over time. 6. The system of claim 1, wherein to interactively present content further includes to determine the content output by the output devices based on the selected prediction type and the selected set of indicators used to determine the estimated future prominence, wherein the selected set of indicators are used to structure the content presented. 7. The system of claim 1, wherein the one or more computing devices are further configured to predict whether the estimated future prominence of the attribute will exceed a prominence threshold. 8. The system of claim 1, wherein the estimated future prominence includes data indicative of (i) the future prominence of the attribute over time and (ii) the future prominence of the attribute over a geospatial distribution. 9. The system of claim 1, wherein the prediction types include at least one of the following: a number of occurrences of the attribute in the future document set, a number of citations in the future document set to a document in the existing document set, a geospatial distribution of occurrences of the attribute in the future document set, a number of times the attribute is published in the future document set, and/or the number of patents issued in the future document set. 10. The system of claim 1, wherein the one or more computing devices are further configured to: generate a geospatial model by dividing a geospatial area into a plurality regions; weight the connectedness between regions based on the number of documents co-authored by individuals from the regions; and determine a geospatial distribution of the attribute based on the occurrences of the attribute in each geospatial region and the connectedness between geospatial regions. 11. A method for predicting prominence of an attribute of at least one document of an existing document set in a future document set with a computing system having one or more computing devices, the method comprising: extracting the attribute from input received by the computing system; based on the attribute, determining a prediction type; based on the prediction type, selecting a set of indicators, each indicator comprising a set of features extracted from the existing document set, the existing document set comprising documents (i) semantically related to the attribute and (ii) published over a time interval beginning prior to and ending on or before a current date; with the indicators, modeling prominence of the attribute in the existing document set over the time interval using one or more statistical modeling techniques; based on the model, estimating future prominence of the attribute in a set of documents that do not currently exist relative to prominence of the attribute in the existing document set; and interactively presenting content relating to the estimated future prominence of the attribute by one or more output devices of the computing system. 12. The method of claim 11, wherein selecting the set of indicators further includes selecting one indicator based on how predictive the selected indicator is of the prediction type and based on how much information the selected indicator conveys about the existing document set. 13. The method of claim 11, further comprising: analyzing each sentence containing the attribute in the existing set of documents; and determining a sentiment parameter for the attribute, the sentiment parameter being indicative of whether an author of the term in the documents of the existing document set liked or disliked the attribute, the sentiment parameter being determined by analyzing each sentence containing the term in the existing set of documents. 14. A prominence predictor system comprising, embodied in one or more non-transitory machine accessible storage media, instructions configured to cause one or more computing devices to: extract an attribute from input received by the one or more computing devices; based on the attribute, determine a prediction type; based on the prediction type, select a set of indicators, each indicator comprising a set of features extracted from an existing document set comprising documents (i) semantically related to the attribute and (ii) published over a time interval beginning prior to and ending on or before a current date; with the indicators, model prominence of the attribute in the existing document set over the time interval using one or more statistical modeling techniques; based on the model, estimate future prominence of the attribute in a future document set that do not currently exist relative to prominence of the attribute in the existing document set; and interactively present content relating to the estimated future prominence of the attribute by one or more output devices of the one or more computing devices. 15. The prominence predictor system of claim 12, further comprising instructions configured to: analyze each sentence containing the attribute in the existing set of documents; and determine a sentiment parameter for the attribute, the sentiment parameter being indicative of whether an author of the term in the documents of the existing document set liked or disliked the attribute, the sentiment parameter being determined by analyzing each sentence containing the term in the existing set of documents. 16. The prominence predictor system of claim 12, further comprising instructions configured to predict whether the estimated future prominence of the attribute will exceed a prominence threshold. 17. The prominence predictor system of claim 12, further comprising instructions configured to (i) estimate the future prominence of the attribute over time and (ii) estimate the future prominence of the attribute over a geospatial distribution. 18. The prominence predictor system of claim 12, wherein: the attribute is associated with a feature extracted from at least one document of the existing document set, the attribute having a number of characteristics including an observed count of occurrences of the attribute in the existing document set, and the prediction type comprises a predicted count of occurrences of the attribute in the future document set based at least in part on the observed count of occurrences of the attribute in the existing document set. 19. The prominence predictor system of claim 12, wherein the set of indicators includes a subset of observable indicators having observed data extracted from the existing document set and a subset of derived indicators having derived data learned from the existing document set, the derived data being semantically determined from the observable data through the use of machine learning techniques. 20. The prominence predictor system of claim 12, wherein indicators of the set of indicators include static features having a single nominal value and dynamic features having values that changes as a function over time.