1. A method comprising: receiving samples comprising temporal windows of token instances to which a user was exposed; the token instances spread over at least a day, and a subset of the token instances originating from a same source and having overlapping instantiation periods; receiving target values representing affective response annotations of the user in response to the temporal windows of token instances; and training an emotional response predictor on input data comprising: the samples, the corresponding target values, and values indicative of the number of token instances in the temporal windows of token instances; wherein the emotional response predictor utilizes the values indicative of the number of the token instances to compensate for non-linear effects resulting from saturation of the user due to the user being exposed to the token instances in the temporal windows of token instances including the token instances originating from the same source and having overlapping instantiation periods.