11. A computer having a memory containing data used to train a situation-dependent machine learning based affective response model for a user, generated by a method comprising: receiving token instances representing stimuli that influence the user's affective state; 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; receiving first data representing levels of user attention in a non-empty subset of the token instances while the user is in the first situation; wherein the first situation is characterized by there being no other person present with the user; receiving second data representing levels of user attention in the non-empty subset of the token instances while the user was in the second situation; wherein the second situation is characterized by there being at least one other person present with the user; receiving a first set of annotations representing emotional states of the user while the user is in the first situation; receiving a 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; and training the situation-dependent machine learning based affective response model for the user with training data comprising the non-empty subset of the token instances, the first and second data, and the first and second sets of annotations.
11. A computer having a memory containing data used to train a situation-dependent machine learning based affective response model for a user, generated by a method comprising: receiving token instances representing stimuli that influence the user's affective state; 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; receiving first data representing levels of user attention in a non-empty subset of the token instances while the user is in the first situation; wherein the first situation is characterized by there being no other person present with the user; receiving second data representing levels of user attention in the non-empty subset of the token instances while the user was in the second situation; wherein the second situation is characterized by there being at least one other person present with the user; receiving a first set of annotations representing emotional states of the user while the user is in the first situation; receiving a 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; and training the situation-dependent machine learning based affective response model for the user with training data comprising the non-empty subset of the token instances, the first and second data, and the first and second sets of annotations. 18. The computer of claim 11 , wherein the token instances originate from multiple distinct token sources and at least some of the token instances comprise representations of elements extracted from digital media content.