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        <title>BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs</title>
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        <namePart type="given">Mathieu</namePart>
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            <namePart type="given">Steven</namePart>
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            <namePart type="given">Marine</namePart>
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            <namePart type="given">Marianna</namePart>
            <namePart type="family">Apidianaki</namePart>
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            <namePart type="given">Saif</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.</abstract>
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%0 Conference Proceedings
%T BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
%A Cliche, Mathieu
%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 cliche-2017-bb
%X In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.
%R 10.18653/v1/S17-2094
%U https://aclanthology.org/S17-2094
%U https://doi.org/10.18653/v1/S17-2094
%P 573-580

Markdown (Informal)

[BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs](https://aclanthology.org/S17-2094) (Cliche, SemEval 2017)

• BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs (Cliche, SemEval 2017)

ACL

• Mathieu Cliche. 2017. BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 573–580, Vancouver, Canada. Association for Computational Linguistics.