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        <title>Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts</title>
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        <namePart type="given">Xingyou</namePart>
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    <name type="personal">
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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    <abstract>Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analysis with comparatively remarkable results. In this paper, we describe a jointed CNN and RNN architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts. Experimental results show an obvious improvement upon the state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28%, 51.50% and 89.95% accuracy, respectively.</abstract>
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        <url>https://aclanthology.org/C16-1229</url>
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%0 Conference Proceedings
%T Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts
%A Wang, Xingyou
%A Jiang, Weijie
%A Luo, Zhiyong
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F wang-etal-2016-combination
%X Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain. In recent years, deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to text sentiment analysis with comparatively remarkable results. In this paper, we describe a jointed CNN and RNN architecture, taking advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN for sentiment analysis of short texts. Experimental results show an obvious improvement upon the state-of-the-art on three benchmark corpora, MR, SST1 and SST2, with 82.28%, 51.50% and 89.95% accuracy, respectively.
%U https://aclanthology.org/C16-1229
%P 2428-2437

Markdown (Informal)

[Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts](https://aclanthology.org/C16-1229) (Wang et al., COLING 2016)

• Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts (Wang et al., COLING 2016)

ACL

• Xingyou Wang, Weijie Jiang, and Zhiyong Luo. 2016. Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2428–2437, Osaka, Japan. The COLING 2016 Organizing Committee.