EKNOT: Event Knowledge from News and Opinions in Twitter
Abstract
We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media's point of view. EKNOT takes a time period as input and outputs a complete picture of the events within the given time range along with the public opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an entity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. Also, if a user is interested in a particular event, he/she can zoom into this event to investigate its aspects (sub-events) summarized in the same manner. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays.
Cite
Text
Li et al. "EKNOT: Event Knowledge from News and Opinions in Twitter." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9826Markdown
[Li et al. "EKNOT: Event Knowledge from News and Opinions in Twitter." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/li2016aaai-eknot/) doi:10.1609/AAAI.V30I1.9826BibTeX
@inproceedings{li2016aaai-eknot,
title = {{EKNOT: Event Knowledge from News and Opinions in Twitter}},
author = {Li, Min and Wang, Jingjing and Tong, Wenzhu and Yu, Hongkun and Ma, Xiuli and Chen, Yucheng and Cai, Haoyan and Han, Jiawei},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {4367-4368},
doi = {10.1609/AAAI.V30I1.9826},
url = {https://mlanthology.org/aaai/2016/li2016aaai-eknot/}
}