1. A computer system configured to store and utilize data to train a machine learning-based affective response model, comprising: a first memory storing data comprising: token instances representing stimuli that influence affective state of a user; the token instances are spread over a period of time that spans at least a day, during which the user is in a first situation and a second situation; wherein the first situation is characterized by there being no other person present with the user, and the second situation is characterized by there being at least one other person present with the user; first data representing levels of user attention in a non-empty subset of the token instances while the user was in the first situation; second data representing levels of user attention in the non-empty subset of the token instances while the user was in the second situation; first set of annotations representing emotional states of the user while the user was in the first situation; second set of annotations representing emotional states of the user while the user was in the second situation; wherein the first set of annotations is not identical to the second set of annotations; first linkage information between the non-empty subset of the token instances, the first data, and the first set of annotations; and second linkage information between the non-empty subset of the token instances, the second data, and the second set of annotations; a second memory storing computer readable instructions, which when executed by a processor, cause the processor to retrieve data stored in the first memory and to utilize the retrieved data to train the machine learning-based affective response model; and a processor configured to execute the instructions stored in the second memory.