The field of automated sentiment analysis has emerged in recent years as an exciting challenge to the computational linguistics community. Research in the field investigates how emotion, bias, mood or affect is expressed in language and how this can be recognised and represented automatically. To date, the most successful applications have been in the classification of product reviews and editorials. This paper aims to open a discussion about alternative evaluation methodologies for sentiment analysis systems that broadens the scope of this new field to encompass existing work in other domains such as psychology and to exploit existing resources in diverse domains such as finance or medicine. We outline some interesting avenues for research which investigate the impact of affective text content on the human psyche and on external factors such as stock markets.