Extracting Irregular Datasets in University Admission Statistics using Text Mining and Benfords Law It is known as Benfordu0027s law that the distribution of the first digits forms a specific shape for natural numerical datasets. Deviation from the Benfordu0027s distribution indicates the irregularity of the dataset. However, it does not tell any clue to interpret the reason of irregularity. The present paper constructs a search engine of cells that appear in tables by correlating a cell with the words in the title of row or column or in the explanation of the table. We generate an exhaustive dataset of cells for testing irregularity by enumerating the search conditions. We applied the method to the number of applicants, the number of candidates, and the number of successful applicants in each department of 565 private universities in Japan. We confirmed the effectiveness of the proposed method by extracting the characteristics of the irregular datasets.

Text Mining and Logic Model for University Midterm Plans National universities in Japan are required to create midterm plans based on logic models. However, universities are still in trouble about what item should be written about five viewpoints of the logic model  purpose, input, activity, output, and outcome. In this paper, we paid attention to the analysis of the third term midterm plan by the expert who extracted the typical sentences which represent the viewpoints. We applied the machine learning for those extracted sentences to identify the characteristic (feature) words and pattern of each viewpoint. It turned out that the activities have simple pattern and can be recognized with high identification performance. However, the output and the outcome are hard to describe the viewpoints.