Messages posted to online social networks (OSN) have recently caused a stir due to the deliberate spread of fake news or rumour. The goal of this research is to understand and analyse the characteristics of fake news, particularly in relation to sentiments, in order to automate the detection of fake news and rumours. We offer a notion that there is a relation between bogus communications or rumours and the sentiments of texts submitted online, based on actual evidence.. We validate our theory by comparing it to cutting-edge baseline text-only fake news detection methods that do not consider sentiments. We ran tests on a standard Twitter fake news dataset and found significant improvements.