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Cluster Computing
Erschienen in: Cluster Computing 4/2020

03.02.2020

A new method of emotional analysis based on CNN–BiLSTM hybrid neural network

verfasst von: Zi-xian Liu, De-gan Zhang, Gu-zhao Luo, Ming Lian, Bing Liu

Erschienen in: Cluster Computing | Ausgabe 4/2020

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Abstract

The hybrid neural network model proposed in this paper consists of two main parts: extracting local features of text vectors by convolutional neural network, extracting global features related to text context by BiLSTM, and fusing the features extracted by the two complementary models. In this paper, the pre-processed sentences are put into the hybrid neural network for training. The trained hybrid neural network can automatically classify the sentences. When testing the algorithm proposed in this paper, the training corpus is Word2vec. The test results show that the accuracy rate of text categorization reaches 94.2%, and the number of iterations is 10. The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced.
Vorheriger Artikel An efficient load balancing system using adaptive dragonfly algorithm in cloud computing
Nächster Artikel A novel deep neural networks based path prediction

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Metadaten
Titel
A new method of emotional analysis based on CNN–BiLSTM hybrid neural network
verfasst von
Zi-xian Liu
De-gan Zhang
Gu-zhao Luo
Ming Lian
Bing Liu
Publikationsdatum
03.02.2020
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 4/2020
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-020-03055-9

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