Identifying aspects at an early stage helps to achieve separation of crosscutting concerns in the initial system analysis, instead of deferring such decisions to later stages of design and code, and thus, having to perform costly refactorings. This paper describes the early-AIM (early aspects identification method) approach that utilises corpus-based natural language processing (NLP) techniques to effectively enable the identification and modelling of early aspects in a semi-automated way.