A text analytics system may predict whether a policy will be adopted. The system may include at least one processor configured to access information scraped from the Internet to identify text data associated with comments expressed by a plurality of individuals about a proposed policy. The at least one processor may be further configured to analyze the text data in order to determine a sentiment of each comment; apply an influence filter to each comment to determine an influence metric associated with each comment; weight each comment using the influence metric; determine based on an aggregate of the weighted comments, an indicator associated with adoption of the policy; and transmit the indicator to a system user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A text analytics system for predicting whether a policy will be adopted, the system comprising: at least one processor configured to: access information scraped from the Internet to identify text data associated with comments expressed by a plurality of individuals about a proposed policy; analyze the text data in order to determine a sentiment of each comment; apply an influence filter to each comment to determine an influence metric associated with each comment; weight each comment using the influence metric; determine based on an aggregate of the weighted comments, an indicator associated with adoption of the policy; and transmit the indicator to a system user. 2. The text analytics system of claim 1, wherein the influence filter accesses a database of terms associated with a heightened degree of influence. 3. The text analytics system of claim 2, wherein the terms associated with a heightened degree of influence include at least one of zip codes, organization names, or addresses. 4. The text analytics system of claim 2, wherein the terms associated with a heightened degree of influence include the author and author related characteristics of the comment. 5. The text analytics system of claim 1, wherein the at least one processor is further configured to apply an association analysis filter to the text data in order to correlate at least a portion of each comment with a particular section of the proposed policy. 6. The text analytics system of claim 1, wherein applying a text analytics filter to the text data in order to determine a sentiment includes matching at least one piece of text data from the comment to at least one other piece of text data stored in a database. 7. The text analytics system of claim 1, wherein the indicator is a likelihood measure reflecting a probability that the policy will be adopted. 8. The text analytics system of claim 1, wherein the policy includes at least one regulation. 9. The text analytics system of claim 1, wherein the policy includes at least one legislative bill. 10. A method for predicting whether a policy will be adopted, the method comprising: accessing information scraped from the Internet to identify text data associated with comments expressed by a plurality of individuals about a proposed policy; analyzing the text data in order to determine a sentiment of each comment; applying an influence filter to each comment to determine an influence metric associated with each comment; weighing each comment using the influence metric; determining based on an aggregate of the weighted comments, an indicator associated with adoption of the policy; and transmitting the indicator to a system user. 11. The method of claim 10, wherein applying an influence filter further comprises: accessing a database of terms associated with a heightened degree of influence. 12. The method of claim 11, wherein the terms associated with a heightened degree of influence include at least one of zip codes, organization names, or addresses. 13. The method of claim 11, wherein the terms associated with a heightened degree of influence include the author and author related characteristics of the comment. 14. The method of claim 10, wherein the method further comprises: applying an association analysis filter to the text data; and correlating at least a portion of each comment with a particular section of the proposed policy. 15. The method of claim 10, wherein applying a text analytics filter to the text data in order to determine a sentiment further comprises: matching at least one piece of text data from the comment to at least one other piece of text data stored in a database. 16. The method of claim 10, wherein the indicator is a likelihood measure reflecting a probability that the regulation will be adopted. 17. The method of claim 10, wherein the policy includes at least one regulation. 18. The method of claim 1, wherein the policy includes at least one legislative bill. 19. A non-transitory computer readable medium storing a program causing a computer to execute a process, the process comprising: accessing information scraped from the Internet to identify text data associated with comments expressed by a plurality of individuals about a proposed policy; analyzing the text data in order to determine a sentiment of each comment; applying an influence filter to each comment to determine an influence metric associated with each comment; weighing each comment using the influence metric; determining based on an aggregate of the weighted comments, an indicator associated with adoption of the policy; and transmitting the indicator to a system user.