18. A portable computing device comprising: a memory configured to store at least one program module; and a processing unit configured to execute the at least one program module to: receive a machine-learned facet model; the machine-learned facet model automatically generated by applying one or more automated machine-learning processes to a plurality of examples of training data to train the machine-learned facet model; the plurality of examples of training data comprising a plurality of automatically clustered and labeled instances of sentiment vocabulary extracted from sentiment bearing content; apply the machine-learned facet model to identify conversational topics and facets associated with one or more segments of a plurality of samples of sentiment-bearing content; determine a consensus for one or more identified facets, the consensus for each facet being based on two or more samples of the sentiment-bearing content; generate a plurality of recommendations about one or more of the identified facets, each of the recommendations being consistent with the corresponding consensus; identify one or more of the recommendations that are relevant to a user; and present one or more of the relevant recommendations by automatically changing a visual appearance of existing content to indicate a type of sentiment associated with one or more corresponding facets identified in the existing content.
18. A portable computing device comprising: a memory configured to store at least one program module; and a processing unit configured to execute the at least one program module to: receive a machine-learned facet model; the machine-learned facet model automatically generated by applying one or more automated machine-learning processes to a plurality of examples of training data to train the machine-learned facet model; the plurality of examples of training data comprising a plurality of automatically clustered and labeled instances of sentiment vocabulary extracted from sentiment bearing content; apply the machine-learned facet model to identify conversational topics and facets associated with one or more segments of a plurality of samples of sentiment-bearing content; determine a consensus for one or more identified facets, the consensus for each facet being based on two or more samples of the sentiment-bearing content; generate a plurality of recommendations about one or more of the identified facets, each of the recommendations being consistent with the corresponding consensus; identify one or more of the recommendations that are relevant to a user; and present one or more of the relevant recommendations by automatically changing a visual appearance of existing content to indicate a type of sentiment associated with one or more corresponding facets identified in the existing content. 20. The portable computing device of claim 18 wherein identifying one or more of the recommendations that are relevant to the user further comprises evaluating a user input in combination with a user profile, and in further combination with one or more grounding factors representing current environmental circumstances associated with the user.