8. The text mining method according to claim 6 , wherein at said text mining step, a word is used as said characteristic, and at said degree of assurance generation step, for each of said plurality of text of the same class, said same-class reliability of said word used as said characteristic and a proximate-word reliability of a word in proximity to the word used as said characteristic are read from said reliability storage unit and for each of said plurality of text of the same class, said degree of assurance is generated based on said same-class reliability of the word used as said characteristic and said proximate-word reliability of the word in proximity to the word used as said characteristic.
6. A text mining method to be executed by a text mining device having a text set storage unit configured to store a plurality of text obtained by forming a plurality of non-text data into the plurality of text and a reliability storage unit configured to store an all-class reliability of each of the plurality of text stored in said text set storage unit, said all-class reliability being derived from the formation of said plurality of non-text data into said plurality of text, including: a text mining step of searching the plurality of text for a plurality of text of a same class, where said plurality of text of the same class has a characteristic the same as the plurality of texts stored in said text set storage unit; and a text selection step of reading, when said plurality of text of the same class are searched, a same-class reliability of each of said plurality of text of the same class from said reliability storage unit, and selecting a part of said plurality of text of the same class from the plurality of text of the same class based on the same-class reliability, wherein said text selection step including: a degree of assurance generation step of generating, for each of said plurality of text of the same class, a degree of assurance indicative of a degree of correlation between said plurality of text of the same class and said plurality of non-text data based on the same-class reliability of said plurality of text of the same class; and a selection step of selecting a portion of said plurality of text of the same class having the highest degree of assurance as said part of said plurality of text of the same class from among said plurality of text of the same class.