@InProceedings{jiang-EtAl:2017:NLPmJ,
  author    = {Jiang, Ye  and  Song, Xingyi  and  Harrison, Jackie  and  Quegan, Shaun  and  Maynard, Diana},
  title     = {Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {25--30},
  abstract  = {News media typically present biased accounts of news stories, and different
	publications present different angles on the same event. In this research, we
	investigate how different publications differ in their approach to stories
	about climate change, by examining the sentiment and topics presented. To
	understand these attitudes, we find sentiment targets by combining Latent
	Dirichlet Allocation (LDA) with SentiWordNet, a general sentiment lexicon.
	Using LDA, we generate topics containing keywords which represent the sentiment
	targets, and then annotate the data using SentiWordNet before regrouping the
	articles based on topic similarity. Preliminary analysis identifies clearly
	different attitudes on the same issue presented in different news sources.
	Ongoing work is investigating how systematic these attitudes are between
	different publications, and how these may change over time.},
  url       = {http://www.aclweb.org/anthology/W17-4205}
}