  Readers take decisions about going through the complete news based on many
factors. The emotional impact of the news title on reader is one of the most
important factors. Cognitive ergonomics tries to strike the balance between
work, product and environment with human needs and capabilities. The utmost
need to integrate emotions in the news as well as advertisements cannot be
denied. The idea is that news or advertisement should be able to engage the
reader on emotional and behavioral platform. While achieving this objective
there is need to learn about reader behavior and use computational psychology
while presenting as well as writing news or advertisements. This paper based on
Machine Learning, tries to map behavior of the reader with the
news/advertisements and also provide inputs for affective value for building
personalized news or advertisements presentations. The affective value of the
news is determined and news artifacts are mapped to reader. The algorithm
suggests the most suitable news for readers while understanding emotional
traits required for personalization. This work can be used to improve reader
satisfaction through embedding emotions in the reading material and
prioritizing news presentations. It can be used to map personal reading
material range, personalized programs and ranking programs, advertisements with
reference to individuals.
