@article{2156,
  author = {Ali Hakimi Parizi, Mohammad Kazemifard, Mohsen Asghari},
  title = {EmoNews: an Emotional News Recommender System},
  journal = {Journal of Digital Information Management},
  year = {2016},
  volume = {14},
  number = {6},
  doi = {},
  url = {http://dline.info/fpaper/jdim/v14i6/jdimv14i6_5.pdf},
  abstract = {In the present study, a system is offered,
which can recommend news regarding the emotion of users
towards the previous news articles the user has searched
so far. This recommendation is in a way, which affects
the user's emotion positively. This study reveals that with
manipulating the recommendation list we can have a
positive impact on the emotion of users. For this purpose,
a news application for android devices is developed, which
can inform users about daily news and recommend some
news to change their emotion toward a positive side. The
proposed system is checked in terms of its engine
performance and influence on their emotion. To evaluate
our system, two groups of people have been chosen which
one of them has used the proposed system and the other
one has used a simple news application. For a period of
more than a month, the emotional impact of the system
has been monitored. Investigations and analyses on the
users given feedbacks indicate the positive effect of the
proposed system on the emotion of users and the alteration
of their emotion towards a positive side is 11 times more
than a simple news application.},
}