Diverse sentiment comparison of news websites over time

Authors:
Jianwei Zhang;Yukiko Kawai;Tadahiko Kumamoto;Shinsuke Nakajima;Yuhki Shiraishi
Affiliations:
Kyoto Sangyo University, Japan;Kyoto Sangyo University, Japan;Chiba Institute of Technology, Japan;Kyoto Sangyo University, Japan;Kyoto Sangyo University, Japan
Venue:
KES-AMSTA'12 Proceedings of the 6th KES international conference on Agent and Multi-Agent Systems: technologies and applications
Year:
2012

Citing 3
Cited 0

Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Opinion Mining and Sentiment Analysis

Foundations and Trends in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Conventional pos-neg model of sentiment analysis primarily for review documents is inappropriate for news articles because of the sentiment diversity of the latter. We design three-dimension sentiments that are more suitable for the analysis of news articles. For a contentious topic, different news websites may have different sentiment tendencies and the tendencies may vary over time. To catch this feature, we construct a sentiment dictionary and develop a system that can extract news articles' sentiments, present sentiment variation over time inside a news website, and compare sentiment correlation between news websites.