Presentation information

Organized Session

Organized Session » [OS] OS-8

[4G2-OS-8a] マイニングと知識創発(1)

Fri. Jun 7, 2019 12:00 PM - 1:40 PM Room G (302A Medium meeting room)

砂山 渡(滋賀県立大学)、加藤 恒昭(東京大学)、西原 陽子(立命館大学)、森 辰則(横浜国立大学)、高間 康史(首都大学東京)

12:20 PM - 12:40 PM

[4G2-OS-8a-02] differential PLSA

A Method of Extracting not Representative Topics but More Individual Topics from Text Data

〇Koji Nomori1 (1. Analytics Design Lab Inc.)

Keywords:PLSA, Text Mining, Topic Model, Patent Analysis

This study proposes a new method extracting topics from text data named differential PLSA. It enables to extract not representative topics but more individual ones. This paper showed the effectiveness by applying the method to patent document data and comparing with results using normal PLSA. As a result, topics extracted by the method were composed of less frequent and more concrete elements, and they were more individual.