Improved Keyword Extraction by Separation into Multiple Document Sets According to Time Series

Abstract

This study proposes a method of extracting keywords including those that appear locally. Useful keyword extraction methods are available for text mining, such as TF-IDF and support vector machine. However, when keywords are extracted on the basis of time series, the local keywords are not often extracted. We propose a method of extracting the local keywords by separating a document set, which we call the document separation approach. The approach splits a document set into multiple sets according to time series, extracts the keywords for each document set, and integrates them. Using 1812 newspaper articles, we experimentally demonstrate that we can extract the local feature keywords using the document separation approach.