SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Keith Cortis; André Freitas; Tobias Daudert; Manuela Huerlimann; MANEL ZARROUK; Siegfried Handschuh; Brian Davis

doi:10.13025/21120

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

Keith Cortis
, André Freitas
, Tobias Daudert
, Manuela Huerlimann
, MANEL ZARROUK
, Siegfried Handschuh
, Brian Davis

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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.

Original language	English (Ireland)
Title of host publication	11th International Workshop on Semantic Evaluations (SemEval-2017)
Publisher	Association for Computational Linguistics
DOIs	https://doi.org/10.13025/21120 https://doi.org/10.18653/v1/s17-2089
Publication status	Published - 1 Jan 2017

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10.13025/21120Licence: CC BY-NC-ND
10.18653/v1/s17-2089

SemEval089 (3)Accepted author manuscript, 262 KBLicence: CC BY-NC-ND

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Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., ZARROUK, MANEL., Handschuh, S., & Davis, B. (2017). SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. In 11th International Workshop on Semantic Evaluations (SemEval-2017) Association for Computational Linguistics. https://doi.org/10.13025/21120, https://doi.org/10.18653/v1/s17-2089

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title = "SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News",

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.",

author = "Keith Cortis and Andr{\'e} Freitas and Tobias Daudert and Manuela Huerlimann and MANEL ZARROUK and Siegfried Handschuh and Brian Davis",

year = "2017",

month = jan,

day = "1",

doi = "10.13025/21120",

language = "English (Ireland)",

booktitle = "11th International Workshop on Semantic Evaluations (SemEval-2017)",

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SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News. / Cortis, Keith; Freitas, André; Daudert, Tobias et al.
11th International Workshop on Semantic Evaluations (SemEval-2017). Association for Computational Linguistics, 2017.

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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AU - Daudert, Tobias

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AU - ZARROUK, MANEL

AU - Handschuh, Siegfried

AU - Davis, Brian

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AB - 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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BT - 11th International Workshop on Semantic Evaluations (SemEval-2017)

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SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

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