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Readers take decisions about going through the complete news based on manyfactors. The emotional impact of the news title on reader is one of the mostimportant factors. Cognitive ergonomics tries to strike the balance betweenwork, product and environment with human needs and capabilities. The utmostneed to integrate emotions in the news as well as advertisements cannot bedenied. The idea is that news or advertisement should be able to engage thereader on emotional and behavioral platform. While achieving this objectivethere is need to learn about reader behavior and use computational psychologywhile presenting as well as writing news or advertisements. This paper based onMachine Learning, tries to map behavior of the reader with thenews/advertisements and also provide inputs for affective value for buildingpersonalized news or advertisements presentations. The affective value of thenews is determined and news artifacts are mapped to reader. The algorithmsuggests the most suitable news for readers while understanding emotionaltraits required for personalization. This work can be used to improve readersatisfaction through embedding emotions in the reading material andprioritizing news presentations. It can be used to map personal readingmaterial range, personalized programs and ranking programs, advertisements withreference to individuals.

Computational Psychology to Embed Emotions into News or Advertisements to Increase Reader Affinity