HCS at SemEval-2017 Task 5

Sentiment Detection in Business News Using Convolutional Neural Networks

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Kuvaus

Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financial
microblogs and news. Our solution for determining the sentiment score extends
an earlier convolutional neural network for sentiment analysis in several ways.
We explicitly encode a focus on a particular company, we apply a data
augmentation scheme, and use a larger data collection to complement the small
training data provided by the task organizers. The best results were achieved
by training a model on an external dataset and then tuning it using the
provided training dataset.
Alkuperäiskielienglanti
Otsikko11th International Workshop on Semantic Evaluations (SemEval-2017) : Proceedings of the Workshop
Sivumäärä5
JulkaisupaikkaStroudsburg, PA
KustantajaAssociation for Computational Linguistics
Julkaisupäiväelokuuta 2017
Sivut842-846
ISBN (elektroninen)978-1-945626-55-5
DOI - pysyväislinkit
TilaJulkaistu - elokuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Workshop on Semantic Evaluations - Vancouver, Kanada
Kesto: 3 elokuuta 20174 elokuuta 2017
Konferenssinumero: 11

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet

Lainaa tätä

Pivovarova, L., Escoter, L., Klami, A., & Yangarber, R. (2017). HCS at SemEval-2017 Task 5: Sentiment Detection in Business News Using Convolutional Neural Networks. teoksessa 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the Workshop (Sivut 842-846). Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.18653/v1/s17-2143
Pivovarova, Lidia ; Escoter, Llorenc ; Klami, Arto ; Yangarber, Roman. / HCS at SemEval-2017 Task 5 : Sentiment Detection in Business News Using Convolutional Neural Networks. 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the Workshop. Stroudsburg, PA : Association for Computational Linguistics, 2017. Sivut 842-846
@inproceedings{cc4bafa5513e47b9b82e268272a55d87,
title = "HCS at SemEval-2017 Task 5: Sentiment Detection in Business News Using Convolutional Neural Networks",
abstract = "Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financialmicroblogs and news. Our solution for determining the sentiment score extendsan earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a dataaugmentation scheme, and use a larger data collection to complement the smalltraining data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.",
keywords = "113 Computer and information sciences",
author = "Lidia Pivovarova and Llorenc Escoter and Arto Klami and Roman Yangarber",
year = "2017",
month = "8",
doi = "10.18653/v1/s17-2143",
language = "English",
pages = "842--846",
booktitle = "11th International Workshop on Semantic Evaluations (SemEval-2017)",
publisher = "Association for Computational Linguistics",
address = "International",

}

Pivovarova, L, Escoter, L, Klami, A & Yangarber, R 2017, HCS at SemEval-2017 Task 5: Sentiment Detection in Business News Using Convolutional Neural Networks. julkaisussa 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the Workshop. Association for Computational Linguistics, Stroudsburg, PA, Sivut 842-846, International Workshop on Semantic Evaluations, Vancouver, Kanada, 03/08/2017. https://doi.org/10.18653/v1/s17-2143

HCS at SemEval-2017 Task 5 : Sentiment Detection in Business News Using Convolutional Neural Networks. / Pivovarova, Lidia; Escoter, Llorenc; Klami, Arto; Yangarber, Roman.

11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the Workshop. Stroudsburg, PA : Association for Computational Linguistics, 2017. s. 842-846.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

TY - GEN

T1 - HCS at SemEval-2017 Task 5

T2 - Sentiment Detection in Business News Using Convolutional Neural Networks

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AU - Klami, Arto

AU - Yangarber, Roman

PY - 2017/8

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N2 - Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financialmicroblogs and news. Our solution for determining the sentiment score extendsan earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a dataaugmentation scheme, and use a larger data collection to complement the smalltraining data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.

AB - Task 5 of SemEval-2017 involves fine-grained sentiment analysis on financialmicroblogs and news. Our solution for determining the sentiment score extendsan earlier convolutional neural network for sentiment analysis in several ways. We explicitly encode a focus on a particular company, we apply a dataaugmentation scheme, and use a larger data collection to complement the smalltraining data provided by the task organizers. The best results were achieved by training a model on an external dataset and then tuning it using the provided training dataset.

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DO - 10.18653/v1/s17-2143

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

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CY - Stroudsburg, PA

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Pivovarova L, Escoter L, Klami A, Yangarber R. HCS at SemEval-2017 Task 5: Sentiment Detection in Business News Using Convolutional Neural Networks. julkaisussa 11th International Workshop on Semantic Evaluations (SemEval-2017): Proceedings of the Workshop. Stroudsburg, PA: Association for Computational Linguistics. 2017. s. 842-846 https://doi.org/10.18653/v1/s17-2143