Text Analysis System, and Characteristic Evaluation System for Message Exchange Using the Same

Abstract

[Problem(s)] To provide a text analysis system that is low cost and able to detect text with a normal expressive or structural features.[Solution] A text analysis system 100 according to the present invention includes a text acquisition portion 110 for acquiring text data; a feature extraction portion 120 for converting the text data acquired by the text acquisition portion 110 into a time series signal to extract a feature from the converted time series signal; a feature storage portion 130 for storing the feature extracted by feature extraction portion 120; and an anomalous text detection portion 140 for detecting anomalous text based on the feature in the feature storage portion 130.

1 - 15 . (canceled) 
     
     
         16 . A text analysis system for analyzing text, the system comprising:
 acquisition means for acquiring text data;   a converter configured to convert characters of the acquired text data into a numerical form to convert the text data into a time series signal;   a feature extractor configured to extract feature information from the time series signal to store the extracted feature information, the feature extractor being further configured to extract a feature from a normalized time series signal of text data described by a normal expressive feature, structural feature, or both, and learn the feature to acquire an output waveform that reproduces an input waveform of the time series signal by using the feature; and   determination means for determining an identity of text data newly acquired by using the feature information.   
     
     
         17 . The text analysis system of  claim 16 , the system further comprising:
 a detector configured to detect anomalous text different from the feature information, based on a determination result by the determination means.   
     
     
         18 . The text analysis system of  claim 16 , wherein the converter is configured to convert characters into numerical data based on a predetermined conversion table. 
     
     
         19 . The text analysis system of  claim 16 , wherein the converter is configured to normalize the time series signal to converge them into a range from a minimal value 0 to a maximum value 1. 
     
     
         20 . The text analysis system of  claim 16 , wherein the converter is configured to attenuate a value of the time series signal that is more than a set threshold to normalize the time series signal. 
     
     
         21 . The text analysis system of  claim 16 , wherein the feature extractor is configured to encode the feature information by an auto-encoder. 
     
     
         22 . The text analysis system of  claim 21 , wherein the feature extractor learns the feature information by a neural network. 
     
     
         23 . A feature evaluation system for message exchange, the feature evaluation system comprising the text analysis system of  claim 17 , wherein the detector is configured to detect an anomaly in a transmitting email based on the determination result by the determination means. 
     
     
         24 . The feature evaluation system of  claim 23 , the feature evaluation system further comprising a transmission controller configured to halt transmission of the transmitting email when the anomaly is detected in the transmitting email. 
     
     
         25 . The feature evaluation system of  claim 24 , the feature evaluation system further comprising a notification means for notifying the halt of transmission of the transmitting email when the transmission of the transmitting email is halted by the transmission controller. 
     
     
         26 - 27 . (canceled) 
     
     
         28 . A text analysis method, the method comprising the steps of:
 acquiring text data;   converting characters of the acquired text data into a numerical form to convert the text data into a time series signal;   extracting feature information from the converted time series signal to store the extracted feature information, wherein extracting the feature information comprises extracting a feature from a normalized time series signal of text data described by a normal expressive feature, a structural feature, or both, and learning the feature to acquire an output waveform that reproduces an input waveform of the time series signal by using the feature; and   determining an identity of newly-acquired text data by using the extracted feature information.   
     
     
         29 . The text analysis method of  claim 28 , wherein the step of determining an identity includes identifying a transmitting email described with an anomalous expressive feature and/or structural feature different from the feature information. 
     
     
         30 - 35 . (canceled)