15. A non-transitory computer-readable medium comprising instructions, which when executed by a computer system causes the computer system to perform operations for training a situation-dependent machine learning based affective response model for a user, the operations comprising: instructions for accessing 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; instructions for accessing 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; instructions for accessing 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; instructions for accessing a first set of annotations representing emotional states of the user while the user is in the first situation; instructions for accessing 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 instructions for training the situation-dependent machine learning based affective response model for the user on 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.
15. A non-transitory computer-readable medium comprising instructions, which when executed by a computer system causes the computer system to perform operations for training a situation-dependent machine learning based affective response model for a user, the operations comprising: instructions for accessing 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; instructions for accessing 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; instructions for accessing 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; instructions for accessing a first set of annotations representing emotional states of the user while the user is in the first situation; instructions for accessing 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 instructions for training the situation-dependent machine learning based affective response model for the user on 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. 16. The non-transitory computer-readable medium of claim 15 , further comprising instructions for accessing first linkage information between the non-empty subset of the token instances, the first data, and the first set of annotations, and instructions for accessing second linkage information between the non-empty subset of the token instances, the second data, and the second set of annotations.