Sentiment Analysis of Movie Reviews Based on CNN-BLSTM - Intelligence Science I (ICIS 2017) Access content directly
Conference Papers Year : 2017

Sentiment Analysis of Movie Reviews Based on CNN-BLSTM

Qianzi Shen
  • Function : Author
  • PersonId : 1033425
Zijian Wang
  • Function : Author
  • PersonId : 1033426
Yaoru Sun
  • Function : Author
  • PersonId : 1033427


Sentiment analysis has been a hot area in the research field of language understanding, but complex deep neural network used in it is still lacked. In this study, we combine convolutional neural networks (CNNs) and BLSTM (bidirectional Long Short-Term Memory) as a complex model to analyze the sentiment orientation of text. First, we design an appropriate structure to combine CNN and BLSTM to find out the most optimal one layer, and then conduct six experiments, including single CNN and single LSTM, for the test and accuracy comparison. Specially, we pre-process the data to transform the words into word vectors to improve the accuracy of the classification result. The classification accuracy of 89.7% resulted from CNN-BLSTM is much better than single CNN or single LSTM. Moreover, CNN with one convolution layer and one pooling layer also performs better than CNN with more layers.
Fichier principal
Vignette du fichier
978-3-319-68121-4_17_Chapter.pdf (463.1 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01820937 , version 1 (22-06-2018)





Qianzi Shen, Zijian Wang, Yaoru Sun. Sentiment Analysis of Movie Reviews Based on CNN-BLSTM. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.164-171, ⟨10.1007/978-3-319-68121-4_17⟩. ⟨hal-01820937⟩
346 View
307 Download



Gmail Facebook X LinkedIn More