@inproceedings{litvak-etal-2016-whats,
title = "What{'}s up on {T}witter? Catch up with {TWIST}!",
author = "Litvak, Marina and
Vanetik, Natalia and
Levi, Efi and
Roistacher, Michael",
editor = "Watanabe, Hideo",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: System Demonstrations",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-2045",
pages = "213--217",
abstract = "Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.",
}
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<abstract>Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.</abstract>
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%0 Conference Proceedings
%T What’s up on Twitter? Catch up with TWIST!
%A Litvak, Marina
%A Vanetik, Natalia
%A Levi, Efi
%A Roistacher, Michael
%Y Watanabe, Hideo
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F litvak-etal-2016-whats
%X Event detection and analysis with respect to public opinions and sentiments in social media is a broad and well-addressed research topic. However, the characteristics and sheer volume of noisy Twitter messages make this a difficult task. This demonstration paper describes a TWItter event Summarizer and Trend detector (TWIST) system for event detection, visualization, textual description, and geo-sentiment analysis of real-life events reported in Twitter.
%U https://aclanthology.org/C16-2045
%P 213-217
Markdown (Informal)
[What’s up on Twitter? Catch up with TWIST!](https://aclanthology.org/C16-2045) (Litvak et al., COLING 2016)
ACL
- Marina Litvak, Natalia Vanetik, Efi Levi, and Michael Roistacher. 2016. What’s up on Twitter? Catch up with TWIST!. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 213–217, Osaka, Japan. The COLING 2016 Organizing Committee.