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

By Xingyou Wang, Weijie Jiang, Zhiyong Luo. 2016

322 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Shuai Wang
2. Lei Zhang
3. Bing Liu
4. B. Liu
5. Linfeng Song
6. Khuong Vo
7. Dang Pham
8. Mao Nguyen

Citations in the corpus

• Sentence-State LSTM for Text Representation (2018, background): “In addition, local ngrams, which have been shown a highly useful source of contextual information for NLP, are not explicitly modelled (Wang et al., 2016).”
• Deep learning for sentiment analysis: A survey (2018, background): “Wang et al. 58 described a joint CNN and RNN architecture for sentiment classification of short texts, which takes advantage of the coarse-grained local features generated by CNN and long-distance dependencies learned via RNN. Guggilla et al. 59 presented a LSTM-and CNN-based deep neural network…”
• Combination of Domain Knowledge and Deep Learning for Sentiment Analysis (2017, methodology): “To store occurrence order relationship between features, recurrent neural network systems such as Long Short Term Memory (LSTM) was used in combination with convolution to perform sentiment analysis for short text [17].”