This paper describes two tools. One is making the network of the relation of language by oneself, and it is a tool which visualizes the self-recognition which nestled up to data rather than analysis of objective text data. Another is a framework for the data acquisition from a web. Although it is already known that web has a lot of data, acquiring it needs special skill. It is facilitated. For text mining, since it is a required tool, it states.

This paper proposes a method for generating linguistic expressions from a time- series data. The proposed method takes differences and similarities among multiple time-series data into consideration: The method generates linguistic expressions by executes three processes sequentially. First, a characteristic such as "rise," "drop," and "stable" is evaluated in each data point of the data series. Second, for each data point in a data series, a weight is assigned by calculating a degree of attention, which is estimated by comparison with another time-series data. Finally, the most pertinent expression is selected.

In this paper, we propose a method for identifying Hiroshima Electric Railway (Hiroden) blogs in a blog database. Hiroden blogs are defined as travel journals that provide regional information along Hiroden streetcar stations. To investigate the effectiveness of our method, we conducted some experiments. From the experimental results, we obtained precision of 82.4% and recall of 64.5% in automatic identification of Hiroden blogs.

An Electronic Medical Record (EMR) records information on patients by computers instead of by paper. Many medical documents, including EMRs that describe the treatment information of patients, are text information. Such text information is complicated. The data arrangement and retrieval of such text parts become difficult because they are often described in a free format; the words, phrases, and expressions are too subjective and reflect each writer. In the present study, we considered the text data of the nursing record within Electronic Medical Records of the University of Miyazaki Hospital.