1. A method for automated language analysis based on word-selection, comprising the steps: a) preparing a computer system ( 1 . 30 ) by aa) storing a plurality of reference language files ( 1 . 10 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ) in order to form a reference sample ( 1 . 40 ), wherein each reference language file ( 1 . 10 ) comprises a minimum number of 100 words, and each reference language file ( 1 . 10 ) originates from a different person having known characteristics, the characteristics including personality traits, ab) storing a dictionary file ( 2 . 20 ) containing a multiplicity of different categories ( 2 . 10 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein all the words in the dictionary file ( 2 . 20 ) are classified in at least one of the categories ( 2 . 10 ), ac) making an individual comparison of each reference language file ( 1 . 10 ) in the reference sample ( 1 . 40 ) with the dictionary file ( 2 . 20 ) by calculating the percentage frequency ( 3 . 40 ) of occurrence of the words in each reference language file ( 1 . 10 ) that are contained in each category ( 2 . 10 ) of the dictionary file ( 2 . 20 ), and ad) storing a set of rules ( 5 . 40 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein the set of rules uses statistical and/or algorithmic methods to calculate associations at least between the percentage frequencies ( 3 . 40 ) calculated in step ac) in one or more categories ( 2 . 10 ) and at least one known characteristic ( 4 . 20 ) of the people from whom the reference language files ( 1 . 10 ) originate, b) following preparation of the computer system in accordance with steps aa)-ad), recording and storing a language file ( 6 . 10 ), in addition to the reference language files ( 1 . 10 ) of the reference sample ( 1 . 40 ), in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein each language file ( 6 . 10 ) and each reference language file is one of a text file or an audio file that is converted into a text file by a transcription, c) analyzing the language file ( 6 . 10 ) additionally recorded and stored in step b), by ca) making an individual comparison of the language file ( 6 . 10 ) with the dictionary file ( 2 . 20 ) by calculating the percentage frequency ( 7 . 30 ) of the words in the language file ( 6 . 10 ) that are contained in each category ( 2 . 10 ) of the dictionary file ( 2 . 20 ), and cb) using the set of rules ( 5 . 40 ) to process the percentage frequencies ( 7 . 30 ) calculated in step ca), wherein the set of rules uses statistical and/or algorithmic methods to examine the percentage frequencies ( 7 . 30 ) calculated in step ca) for similarities with the percentage frequencies ( 3 . 40 ) calculated in step ac), and classifies the language file ( 6 . 10 ) according to the established similarities, and associates said file with at least one known characteristic belonging to the people from whom the reference language files ( 1 . 10 ) originate, d) creating an output file ( 8 . 20 ), which contains characteristics ( 4 . 20 ) associated with the language file ( 6 . 10 ) in step cb), and e) outputting the output file ( 8 . 20 ) by displaying the output file on a screen, outputting the output file using a speaker, transmitting the output file via a network, or printing the output file using a printer, f) fa) expanding the reference sample ( 1 . 40 ) in step aa) by adding as reference language files ( 1 . 10 ), each language file ( 6 . 10 ) recorded in step b), fb) providing a feedback through an input, which allows an evaluation of the correctness of the analysis of step c), and fc) updating and re-saving the set of rules ( 5 . 40 ) taking into account the enlarged database from step ad).
1. A method for automated language analysis based on word-selection, comprising the steps: a) preparing a computer system ( 1 . 30 ) by aa) storing a plurality of reference language files ( 1 . 10 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ) in order to form a reference sample ( 1 . 40 ), wherein each reference language file ( 1 . 10 ) comprises a minimum number of 100 words, and each reference language file ( 1 . 10 ) originates from a different person having known characteristics, the characteristics including personality traits, ab) storing a dictionary file ( 2 . 20 ) containing a multiplicity of different categories ( 2 . 10 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein all the words in the dictionary file ( 2 . 20 ) are classified in at least one of the categories ( 2 . 10 ), ac) making an individual comparison of each reference language file ( 1 . 10 ) in the reference sample ( 1 . 40 ) with the dictionary file ( 2 . 20 ) by calculating the percentage frequency ( 3 . 40 ) of occurrence of the words in each reference language file ( 1 . 10 ) that are contained in each category ( 2 . 10 ) of the dictionary file ( 2 . 20 ), and ad) storing a set of rules ( 5 . 40 ) in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein the set of rules uses statistical and/or algorithmic methods to calculate associations at least between the percentage frequencies ( 3 . 40 ) calculated in step ac) in one or more categories ( 2 . 10 ) and at least one known characteristic ( 4 . 20 ) of the people from whom the reference language files ( 1 . 10 ) originate, b) following preparation of the computer system in accordance with steps aa)-ad), recording and storing a language file ( 6 . 10 ), in addition to the reference language files ( 1 . 10 ) of the reference sample ( 1 . 40 ), in a memory unit ( 1 . 20 ) of the computer system ( 1 . 30 ), wherein each language file ( 6 . 10 ) and each reference language file is one of a text file or an audio file that is converted into a text file by a transcription, c) analyzing the language file ( 6 . 10 ) additionally recorded and stored in step b), by ca) making an individual comparison of the language file ( 6 . 10 ) with the dictionary file ( 2 . 20 ) by calculating the percentage frequency ( 7 . 30 ) of the words in the language file ( 6 . 10 ) that are contained in each category ( 2 . 10 ) of the dictionary file ( 2 . 20 ), and cb) using the set of rules ( 5 . 40 ) to process the percentage frequencies ( 7 . 30 ) calculated in step ca), wherein the set of rules uses statistical and/or algorithmic methods to examine the percentage frequencies ( 7 . 30 ) calculated in step ca) for similarities with the percentage frequencies ( 3 . 40 ) calculated in step ac), and classifies the language file ( 6 . 10 ) according to the established similarities, and associates said file with at least one known characteristic belonging to the people from whom the reference language files ( 1 . 10 ) originate, d) creating an output file ( 8 . 20 ), which contains characteristics ( 4 . 20 ) associated with the language file ( 6 . 10 ) in step cb), and e) outputting the output file ( 8 . 20 ) by displaying the output file on a screen, outputting the output file using a speaker, transmitting the output file via a network, or printing the output file using a printer, f) fa) expanding the reference sample ( 1 . 40 ) in step aa) by adding as reference language files ( 1 . 10 ), each language file ( 6 . 10 ) recorded in step b), fb) providing a feedback through an input, which allows an evaluation of the correctness of the analysis of step c), and fc) updating and re-saving the set of rules ( 5 . 40 ) taking into account the enlarged database from step ad). 16. The method as claimed in claim 1 , wherein the categories include at least one of different parts of speech, different semantic fields, and different n-grams.