Recent developments of quantitative analyses for explorations of changes in writers' mental condition, emotion, and thought have attracted much attention. Here, we report a quantitative approach for estimating ‘the possible time of the literature style evolution in Koji Uno’s works’. Koji Uno, a well-known Japanese litterateur, suffered mental illness in 1927 and stopped his writing about 6 years because of the disease treatment and recovery. It was reported that his literature style changed when he restarted his writing after recovering from the illness. However, the work named ‘Nichiyobi’ which had been published before the disease treatment is much more similar with the works written after recovering. For understating the time of the literature style changed in Uno’s works, we studied four features in the published writings of Uno before the illness using discrimination analysis method, which suggested that the writing style has been changed before the disease treatment.

 Offender profiling is a method used to assist criminal investigation teams by estimating an offender’s gender, age, or job, on the basis of analyzing the crime scene using statistical and psychological methods. If only printed documents or e-mails are available, however, analysts are powerless to estimate the offenders’ characteristics until now, because there is no crime scene. This study aims to estimate gender by applying a random forest technique to texts on Blog. The results indicated that the following stylometric features were effective in estimating gender: rate of usage of Kanji, Hiragana, Katakana, nouns. Moreover, the frequency of certain parts of speech (verb, adjective, postpositional particle, and interjection), conjunctive particle ｢し｣, auxiliary verb ｢なかっ｣, comma, and letters (｢私｣｢僕｣｢っ｣｢ゃ｣) also were effective. The results of Leave-One-Out-Cross-Validation (LOOCV) showed that the highest rate of accuracy was 86.0%: 84.6% for male and 87.5% for female in the rate of precision. Furthermore, support vector machine showed lower accuracy, 75.0%, comparing with random forest: 69.2% for male and 85.7% for female in the rate of precision