An objective of the present disclosure is to acquire embedding vectors in which features of targets fluctuating in price depending on dates are embedded. When a set of texts ni released from a past date to a base date are input, a neural network outputs a classification y{circumflex over ( )}jt indicating whether the price of each target has increased or decreased since the previous date until the base date. An information processing device achieving the neural network trains a model including embedding vectors. That is, the information processing device extracts feature vectors nKi and nVi at two different levels from each text ni released at each date, determines a weight αji, based on the inner product of the feature vector nKi and an embedding vector sj, determines a status mjτ by multiplying the other feature vector nKi by the weight αji and taking the sum, and inputs the status mjτ to a classifier, which is caused to output a classification y{circumflex over ( )}jt.