Trillions of data are being created every day on the Internet due to the growing number of social platforms on the World Wide Web (WWW). Processed data when given in context makes information of any knowledge. However, irresponsible use of the data or misinterpretation of data could be the reasons for false information dissemination. Many researchers from various fields, such as computer science and social science, draw their focus on assessing the veracity of information. There are many techniques to perceive this topic, for instance, social network behaviour, and semantic analysis. The common practice is using semantic analysis approach, where the syntactic structure is analysed and polarity of the texts is determined. In this paper, we approach the veracity assessment by using emotion analysis. We identified emotional states conveyed in news content and calculated the weight of each state in each news content. Contrary to popular belief, our finding showed that emotional, or affective states conveyed in false news are varied - positive and negative states. The distinct feature is the weight of the states in news content. Using multi-layer perceptron, we classified the news and achieved 90% accuracy with our collected dataset and 85% using LIAR dataset.