Abstract
Text sentiment analysis is used to discover the public’s appreciation and preferences for specific events. In order to effectively extract the deep semantic features of sentences and reduce the dependence of long distance information dependency, two models based on convolutional neural network and bidirectional long short-term memory model, CNN-BLSTM and BLSTM-CNN are proposed. Convolutional neural networks can get text features better. Bidirectional long-short time memory model can not only capture long-range information and solve gradient attenuation problem, but also represent future contextual information semantics of word sequence better. These two network architectures are explained in detail in this paper, and performed comparisons against some normal methods, such as methods based emotion lexicon, machine learning methods, LSTM and other neural network models. Experiments show that these two proposed models have achieved better results in text sentiment analysis. The best model CNN-BLSTM is better than the normal neural network models in accuracy.
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Song, M., Zhao, X., Liu, Y., Zhao, Z. (2018). Text Sentiment Analysis Based on Convolutional Neural Network and Bidirectional LSTM Model. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_6
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DOI: https://doi.org/10.1007/978-981-13-2206-8_6
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