A method of evaluating an electronic document to be posted on a computer network, the method performed by an analysis server 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 the responses to the posting of the electronic document, the evaluating the features in the set further comprising: assigning weights to features in the set of features based on coefficients identified through the one or more machine-learned models, wherein a sign of a weight assigned to a feature indicates the direction of the correlation between the feature and the responses to the electronic document, and the absolute value of the weight assigned to the feature indicates the degree of correlation between the feature and the responses to the electronic document; and summing the weights of the set of features to predict the responses to the electronic document; and filtering out a subset of the features; and providing the predicted responses and the filtered subset of features (422, 426) for display to the client device. 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 the responses to the posting of the electronic document, the evaluating the features in the set further comprising: assigning weights to features in the set of features based on coefficients identified through the one or more machine-learned models, wherein a sign of a weight assigned to a feature indicates the direction of the correlation between the feature and the responses to the electronic document, and the absolute value of the weight assigned to the feature indicates the degree of correlation between the feature and the responses to the electronic document; and summing the weights of the set of features to predict the responses to the electronic document; and assigning weights to features in the set of features based on coefficients identified through the one or more machine-learned models, wherein a sign of a weight assigned to a feature indicates the direction of the correlation between the feature and the responses to the electronic document, and the absolute value of the weight assigned to the feature indicates the degree of correlation between the feature and the responses to the electronic document; and summing the weights of the set of features to predict the responses to the electronic document; and filtering out a subset of the features; and providing the predicted responses and the filtered subset of features (422, 426) for display to the client device.