Comparing Attitudes to Climate Change in the Media using sentiment analysis based on Latent Dirichlet Allocation

Ye Jiang, Xingyi Song, Jackie Harrison, Shaun Quegan, Diana Maynard


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.
Anthology ID:
W17-4205
Volume:
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
25–30
Language:
URL:
https://www.aclweb.org/anthology/W17-4205
DOI:
10.18653/v1/W17-4205
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4205.pdf