TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter

Bo Wang, Maria Liakata, Adam Tsakalidis, Spiros Georgakopoulos Kolaitis, Symeon Papadopoulos, Lazaros Apostolidis, Arkaitz Zubiaga, Rob Procter, Yiannis Kompatsiaris


Abstract
We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets. We describe the main elements of the system and illustrate its functionality with two examples of sentiment analysis of topics related to the 2017 UK general election.
Anthology ID:
I17-3006
Volume:
Proceedings of the IJCNLP 2017, System Demonstrations
Month:
November
Year:
2017
Address:
Tapei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–24
Language:
URL:
https://aclanthology.org/I17-3006
DOI:
Bibkey:
Cite (ACL):
Bo Wang, Maria Liakata, Adam Tsakalidis, Spiros Georgakopoulos Kolaitis, Symeon Papadopoulos, Lazaros Apostolidis, Arkaitz Zubiaga, Rob Procter, and Yiannis Kompatsiaris. 2017. TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter. In Proceedings of the IJCNLP 2017, System Demonstrations, pages 21–24, Tapei, Taiwan. Association for Computational Linguistics.
Cite (Informal):
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter (Wang et al., IJCNLP 2017)
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PDF:
https://aclanthology.org/I17-3006.pdf