@inproceedings{cortis-etal-2017-semeval,
title = "{S}em{E}val-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",
author = "Cortis, Keith and
Freitas, Andr{\'e} and
Daudert, Tobias and
Huerlimann, Manuela and
Zarrouk, Manel and
Handschuh, Siegfried and
Davis, Brian",
editor = "Bethard, Steven and
Carpuat, Marine and
Apidianaki, Marianna and
Mohammad, Saif M. and
Cer, Daniel and
Jurgens, David",
booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S17-2089",
doi = "10.18653/v1/S17-2089",
pages = "519--535",
abstract = "This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.",
}
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<abstract>This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.</abstract>
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%0 Conference Proceedings
%T SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
%A Cortis, Keith
%A Freitas, André
%A Daudert, Tobias
%A Huerlimann, Manuela
%A Zarrouk, Manel
%A Handschuh, Siegfried
%A Davis, Brian
%Y Bethard, Steven
%Y Carpuat, Marine
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y Cer, Daniel
%Y Jurgens, David
%S Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F cortis-etal-2017-semeval
%X This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.
%R 10.18653/v1/S17-2089
%U https://aclanthology.org/S17-2089
%U https://doi.org/10.18653/v1/S17-2089
%P 519-535
Markdown (Informal)
[SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News](https://aclanthology.org/S17-2089) (Cortis et al., SemEval 2017)
ACL