Computational Psychology to Embed Emotions into News or Advertisements to Increase Reader Affinity
This work addresses the need for increased reader affinity in media and advertising through emotional personalization, but it appears incremental as it builds on existing computational psychology and machine learning techniques without claiming major breakthroughs.
The paper tackles the problem of low reader engagement by proposing a machine learning approach to embed emotions into news and advertisements, mapping reader behavior to content to personalize presentations and improve satisfaction.
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.