SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning

Zinedine Rebiai; Simon Andersen; Antoine Debrenne; Victor Lafargue

doi:10.18653/v1/S19-2051

SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning

Zinedine Rebiai, Simon Andersen, Antoine Debrenne, Victor Lafargue

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Abstract

In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1μ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1μ of 0.7324 on the test dataset.

Anthology ID:: S19-2051
Volume:: Proceedings of the 13th International Workshop on Semantic Evaluation
Month:: June
Year:: 2019
Address:: Minneapolis, Minnesota, USA
Editors:: Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 297–301
Language:
URL:: https://aclanthology.org/S19-2051/
DOI:: 10.18653/v1/S19-2051
Bibkey:
Cite (ACL):: Zinedine Rebiai, Simon Andersen, Antoine Debrenne, and Victor Lafargue. 2019. SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 297–301, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning (Rebiai et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2051.pdf

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@inproceedings{rebiai-etal-2019-scia,
    title = "{SCIA} at {S}em{E}val-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning",
    author = "Rebiai, Zinedine  and
      Andersen, Simon  and
      Debrenne, Antoine  and
      Lafargue, Victor",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2051/",
    doi = "10.18653/v1/S19-2051",
    pages = "297--301",
    abstract = "In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1{\ensuremath{\mu}}) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1{\ensuremath{\mu}} of 0.7324 on the test dataset."
}

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%0 Conference Proceedings
%T SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning
%A Rebiai, Zinedine
%A Andersen, Simon
%A Debrenne, Antoine
%A Lafargue, Victor
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F rebiai-etal-2019-scia
%X In this paper we present our submission for SemEval-2019 Task 3: EmoContext. The task consisted of classifying a textual dialogue into one of four emotion classes: happy, sad, angry or others. Our approach tried to improve on multiple aspects, preprocessing with an emphasis on spell-checking and ensembling with four different models: Bi-directional contextual LSTM (BC-LSTM), categorical Bi-LSTM (CAT-LSTM), binary convolutional Bi-LSTM (BIN-LSTM) and Gated Recurrent Unit (GRU). On the leader-board, we submitted two systems that obtained a micro F1 score (F1\ensuremathμ) of 0.711 and 0.712. After the competition, we merged our two systems with ensembling, which achieved a F1\ensuremathμ of 0.7324 on the test dataset.
%R 10.18653/v1/S19-2051
%U https://aclanthology.org/S19-2051/
%U https://doi.org/10.18653/v1/S19-2051
%P 297-301

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Markdown (Informal)

[SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning](https://aclanthology.org/S19-2051/) (Rebiai et al., SemEval 2019)

SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning (Rebiai et al., SemEval 2019)

ACL

Zinedine Rebiai, Simon Andersen, Antoine Debrenne, and Victor Lafargue. 2019. SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 297–301, Minneapolis, Minnesota, USA. Association for Computational Linguistics.