In recent years, sentiment analysis (SA) has emerged as a rapidly expanding field of application and research in the area of information retrieval. In order to facilitate the task of selecting lexical resources for automated SA systems, this paper sets out a detailed analysis of four widely used sentiment lexica. The analysis provides an overview of the coverage of each lexicon individually, the overlap and consistency of the four resources and a corpus analysis of the distribution of the resources’ lexical contents in general and specialised language. This work aims to explore the characteristics of affective language as represented by these lexica and the implications of the findings for developers of SA systems.