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Sentiment classification of short text is a challenging task because of limited contextual information. We propose a capsule-based hybrid neural network model which can obtain the implicit semantic information effectively. Bidirectional gated recurrent unit (BGRU) is applied in this model to achieve the interdependent features with long distance. Moreover, the capsule network can extract richer textual information to improve expression ability. Compared with the attention-based model which combines self-attention mechanisms and convolutional neural networks (CNN), the capsule-based hybrid model has the advantage of less training time and simple network structure to achieve better performance. The performance is evaluated on two short text review datasets. Our capsule-based model outperforms other related models on movie review data and gets the highest accuracy of 0.8255. Meanwhile, it performs better than most of the systems in NLPCC2014 Task II and, especially achieves the best result on negative data. INDEX TERMS Sentiment classification, capsule network, bidirectional gated recurrent unit, deep learning.

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Energy harvesting devices made of piezoelectric material are highly anticipated energy sources for power wireless sensors. Tremendous efforts have been made to improve the performance of piezoelectric energy harvesters (PEHs). Noticeably, topology optimization has shown an attractive potential to design PEHs with enhanced energy conversion efficiency. In this work, an alternative yet more practical design objective was considered, where the open-circuit voltage of PEHs is enhanced by topologically optimizing the through-thickness piezoelectric material distribution of plate-type PEHs subjected to harmonic excitations. Compared to the conventional efficiency-enhanced designs, the open-circuit voltage of PEHs can be evidently enhanced by the proposed method while with negligible sacrifice on the energy conversion efficiency. Numerical investigations show that the voltage cancellation effect due to inconsistent voltage phases can be effectively ameliorated by optimally distributed piezoelectric materials.

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