Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts

Xingyou Wang, Weijie Jiang, Zhiyong Luo


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.
Anthology ID:
C16-1229
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2428–2437
Language:
URL:
https://aclanthology.org/C16-1229
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts (Wang et al., COLING 2016)
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PDF:
https://preview.aclanthology.org/update-css-js/C16-1229.pdf
Code
 ultimate010/crnn
Data
SST