Identifying dominant topics appearing in the Journal of Cleaner Production

The number of publications in the field of sustainability research has increased rapidly in recent decades and the research topics have multiplied dramatically. It has become difficult to keep track of this highly dynamic field of research. In order to explore the possibilities of computer-aided automated text and meaning capture for the field of sustainability research, we are testing in this paper the method of Latent Semantic Analysis (LSA) with regard to the text corpus published by the Journal of Cleaner Production since 1995. We present the discernible topics identified by this method both in their statistical concept composition and in their temporal evolution and analyze individual, randomly selected contributions in relation to their thematic position in the overall corpus. In particular, the latter gives hope that text mining methods like the here applied LSA could help human readers in the future to maintain an overview in large text corpora and to categorize individual contributions thematically. In this study, as regards content, we identified sustainability education as crucial topic for sustainable development and, additionally, that life-cycle analyses are significantly gaining importance in recent years.