32. The information analysis method according to claim 22 , further comprising the steps of: (d) generating training data from training text information including information indicating whether or not each sentence of the training text information is the target information, by generating a plurality of units of training each composed of a plurality of sentences of the training text information, from the training text information in accordance with a setting condition, setting, for each unit of training, a target information density indicating an amount of the target information included in the unit of training, with reference to information indicating whether or not each sentence of the unit of training is the target information, further obtaining, for each unit of training, a feature quantity from information acquired from a word or a clause in each sentence of the unit of training, and generating the target information density and the feature quantity of each unit of training as the training data; and (e) learning a density estimation model usable in the density estimation performed in the step (a), using the training data generated in the step (d), wherein in the step (a), for each unit of analysis, the density is estimated in accordance with the density estimation model acquired in the step (e).
32. The information analysis method according to claim 22 , further comprising the steps of: (d) generating training data from training text information including information indicating whether or not each sentence of the training text information is the target information, by generating a plurality of units of training each composed of a plurality of sentences of the training text information, from the training text information in accordance with a setting condition, setting, for each unit of training, a target information density indicating an amount of the target information included in the unit of training, with reference to information indicating whether or not each sentence of the unit of training is the target information, further obtaining, for each unit of training, a feature quantity from information acquired from a word or a clause in each sentence of the unit of training, and generating the target information density and the feature quantity of each unit of training as the training data; and (e) learning a density estimation model usable in the density estimation performed in the step (a), using the training data generated in the step (d), wherein in the step (a), for each unit of analysis, the density is estimated in accordance with the density estimation model acquired in the step (e). 34. The information analysis method according to claim 32 , wherein in the step (d), the plurality of units of training are generated by, for each unit of training, extracting a preset number of consecutive sentences from a plurality of consecutive sentences of the training text information and generating the unit of training from the extracted consecutive sentences, and a feature quantity is further obtained for each unit of training from one or more types of information that include information acquired from a word or a clause in each sentence of the unit of training and information acquired by comparing the training text information and each sentence of the unit of training.