  Emotion Generating Calculations (EGC) method based on the Emotion Eliciting
Condition Theory can decide whether an event arouses pleasure or not and
quantify the degree under the event. An event in the form of Case Frame
representation is classified into 12 types of calculations. However, the weak
point in EGC is Favorite Value (FV) as the personal taste information. In order
to improve the problem, this paper challenges to establish a learning method to
learn speaker's taste information from dialog. Especially, the learning method
employs Fuzzy Petri Net to find an appropriate FV to a word which has the
unknown FV. This paper discusses the effective learning method to improve a
weak point of EGC when a missing value of FV exists.
