Evaluations of a document are generated that indicate likelihoods of the document achieving its objectives. The evaluations are based on predictive characteristics of one or more outcomes of the client document that are indicative of whether the document will achieve its objectives. Specifically, a server receives the document from a client device. The server extracts a set of features from the document. The evaluations of the document are generated based on the predictive characteristics for the one or more outcomes of the document. The generated evaluations are provided to the client device such that the document can be optimized to achieve its desired objectives. The optimized document may also be sent to a posting server for posting to a computer network. The known outcomes of the optimized document are collected through reader responses to the document and analyzed to improve evaluations for other documents.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of evaluating an electronic document with respect to an objective, comprising: receiving the electronic document from a client device via a computer network, the electronic document having content directed toward achieving an objective; extracting a set of features from the content of the electronic document; evaluating the features in the set using one or more machine-learned models that indicate directions and degrees of correlation between the features extracted from the content of the electronic document and the objective to which the content of the document is directed to predict an outcome of the electronic document with respect to the objective; and providing the predicted outcome to the client device. 2. The method of claim 1, further comprising: posting the electronic document on the computer network; receiving outcome data describing responses to the posting of the electronic document on the computer network with respect to the objective; and selectively revising the machine-learned models based on the outcome data. 3. The method of claim 1, further comprising: generating a training corpus of electronic documents and associated known outcome data describing known outcomes resulting from postings of the electronic documents on the computer network; extracting the set of features from contents of the electronic documents in the training corpus; and generating the one or more machine-learned models by correlating the sets of features extracted from contents of the electronic documents in the training corpus and the associated known outcome data. 4. The method of claim 1, wherein extracting the set of features from the content of the electronic document comprises: extracting syntactic factors describing a structure of sentences in the content of the electronic document; extracting structural factors relating to structure and layout of the content of the electronic document; and extracting semantic factors relating to meaning of the content in the electronic document. 5. The method of claim 1, wherein extracting the set of features from the content of the electronic document comprises: extracting phrase-related features indicating presence of distinctive phrases in the content of the electronic document, the phrase-related features indicating categories corresponding to the distinctive phrases and the distinctive phrases' levels of association with the corresponding categories. 6. The method of claim 1, wherein providing the predicted outcome to the client device comprises: providing a user interface for display on the client device, the user interface identifying a plurality of distinctive phrases in the content of the electronic document and, for each identified distinctive phrase, indicating an influence of the distinctive phrase on the predicted outcome of the electronic document with respect to the objective. 7. The method of claim 1, wherein the electronic document is a recruiting document, the objective relates to demographic information of people responding to the recruiting document, and the machine-learned models indicate directions and degrees of correlation between phrases in recruiting documents and demographic information of people who respond to the recruiting documents. 8. A non-transitory computer-readable storage medium storing executable computer program instructions for evaluating an electronic document with respect to an objective, the computer program instructions comprising: receiving the electronic document from a client device via a computer network, the electronic document having content directed toward achieving an objective; extracting a set of features from the content of the electronic document; evaluating the features in the set using one or more machine-learned models that indicate directions and degrees of correlation between the features extracted from the content of the electronic document and the objective to which the content of the document is directed to predict an outcome of the electronic document with respect to the objective; and providing the predicted outcome to the client device. 9. The computer-readable storage medium of claim 8, the computer program instructions further comprising: posting the electronic document on the computer network; receiving outcome data describing responses to the posting of the electronic document on the computer network with respect to the objective; and selectively revising the machine-learned models based on the outcome data. 10. The computer-readable storage medium of claim 8, the computer program instructions further comprising: generating a training corpus of electronic documents and associated known outcome data describing known outcomes resulting from postings of the electronic documents on the computer network; extracting the set of features from contents of the electronic documents in the training corpus; and generating the one or more machine-learned models by correlating the sets of features extracted from contents of the electronic documents in the training corpus and the associated known outcome data. 11. The computer-readable storage medium of claim 8, wherein extracting the set of features from the content of the electronic document comprises: extracting syntactic factors describing a structure of sentences in the content of the electronic document; extracting structural factors relating to structure and layout of the content of the electronic document; and extracting semantic factors relating to meaning of the content in the electronic document. 12. The computer-readable storage medium of claim 8, wherein extracting the set of features from the content of the electronic document comprises: extracting phrase-related features indicating presence of distinctive phrases in the content of the electronic document, the phrase-related features indicating categories corresponding to the distinctive phrases and the distinctive phrases' levels of association with the corresponding categories. 13. The computer-readable storage medium of claim 8, wherein providing the predicted outcome to the client device comprises: providing a user interface for display on the client device, the user interface identifying a plurality of distinctive phrases in the content of the electronic document and, for each identified distinctive phrase, indicating an influence of the distinctive phrase on the predicted outcome of the electronic document with respect to the objective. 14. The computer-readable storage medium of claim 8, wherein the electronic document is a recruiting document, the objective relates to demographic information of people responding to the recruiting document, and the machine-learned models indicate directions and degrees of correlation between phrases in recruiting documents and demographic information of people who respond to the recruiting documents. 15. An analysis server for evaluating an electronic document with respect to an objective, comprising: a processor for executing computer program instructions; and a non-transitory computer-readable storage medium storing computer program instructions executable to perform steps comprising: receiving the electronic document from a client device via a computer network, the electronic document having content directed toward achieving an objective; extracting a set of features from the content of the electronic document; evaluating the features in the set using one or more machine-learned models that indicate directions and degrees of correlation between the features extracted from the content of the electronic document and the objective to which the content of the document is directed to predict an outcome of the electronic document with respect to the objective; and providing the predicted outcome to the client device. 16. The analysis server of claim 15, the computer program instructions further comprising: posting the electronic document on the computer network; receiving outcome data describing responses to the posting of the electronic document on the computer network with respect to the objective; and selectively revising the machine-learned models based on the outcome data. 17. The analysis server of claim 15, the computer program instructions further comprising: generating a training corpus of electronic documents and associated known outcome data describing known outcomes resulting from postings of the electronic documents on the computer network; extracting the set of features from contents of the electronic documents in the training corpus; and generating the one or more machine-learned models by correlating the sets of features extracted from contents of the electronic documents in the training corpus and the associated known outcome data. 18. The analysis server of claim 15, the computer program instructions further comprising: extracting syntactic factors describing a structure of sentences in the content of the electronic document; extracting structural factors relating to structure and layout of the content of the electronic document; and extracting semantic factors relating to meaning of the content in the electronic document. 19. The analysis server of claim 15, the computer program instructions further comprising: extracting phrase-related features indicating presence of distinctive phrases in the content of the electronic document, the phrase-related features indicating categories corresponding to the distinctive phrases and the distinctive phrases' levels of association with the corresponding categories. 20. The analysis server of claim 15, the computer program instructions further comprising: providing a user interface for display on the client device, the user interface identifying a plurality of distinctive phrases in the content of the electronic document and, for each identified distinctive phrase, indicating an influence of the distinctive phrase on the predicted outcome of the electronic document with respect to the objective.