A Survey of Sentiment Analysis Based on Transfer Learning <s> 3) HYBRID NEURAL NETWORK MODELS <s> We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it has a hierarchical structure that mirrors the hierarchical structure of documents; (ii) it has two levels of attention mechanisms applied at the wordand sentence-level, enabling it to attend differentially to more and less important content when constructing the document representation. Experiments conducted on six large scale text classification tasks demonstrate that the proposed architecture outperform previous methods by a substantial margin. Visualization of the attention layers illustrates that the model selects qualitatively informative words and sentences. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> 3) HYBRID NEURAL NETWORK MODELS <s> We introduce a deep memory network for aspect level sentiment classification. Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect. Such importance degree and text representation are calculated with multiple computational layers, each of which is a neural attention model over an external memory. Experiments on laptop and restaurant datasets demonstrate that our approach performs comparable to state-of-art feature based SVM system, and substantially better than LSTM and attention-based LSTM architectures. On both datasets we show that multiple computational layers could improve the performance. Moreover, our approach is also fast. The deep memory network with 9 layers is 15 times faster than LSTM with a CPU implementation. <s> BIB002
In this paper, the hybrid model is defined as a combined system, which is a combination of network models or mechanisms other than CNN and RNN. The emergence of hybrid models is due to the fact that existing traditional models cannot fully meet the task requirements, and people expect to improve the accuracy of text analysis from other angles than just the improvement of a single model. In general, the hybrid model is dominated by a neural network model, and other mechanisms are added to achieve higher performance based on the shortcomings. With the diversified development of sentiment analysis needs, the hybrid models have occupied an increasingly important position. By combining the two models or citing other algorithms in a single model, the analysis accuracy of the single model can be further improved. The paper recently applied a hybrid model to sentiment analysis to deepen the people's understanding. Akhtar et al.  proposed a hybrid deep learning architecture, first learning the sentiment embedding vector from the convolutional neural network (CNN), and then selecting a set of optimization features through the multi-objective optimization (MOO) framework, finally SVM is used to classify the sentiment augmented optimized vector. This is the first attempt to apply this deep learning model to sentiment analysis in languages with less resources. Yang et al. BIB001 formulated a hierarchical attentional network for document classification. A two-level attention mechanism is adopted at the word and sentence level. The visualization of the attention layer indicates the specific words selected by the model. Naturally, the benefit of joining the attention mechanism is the ability to intuitively explain the importance of individual sentences and words to the classification category. Traditional neural models such as LSTM capture context information implicitly, do not explicitly display important contextual cues for specific aspects of the text, and use the same operations for each context word, so it cannot explicitly show the importance of each contextual word. The ideal solution should be able to explicitly capture the importance of contextual words. Therefore, Tang et al. BIB002 proposed a sentiment analysis of aspect-level text using the Deep Memory Network, which explicitly captures the importance of each context word when inferring the level of emotional polarity.The effect is much better than LSTM and attentionbased LSTM. The hybrid system can combine the advantages of different models to maximize model performance. Practice has proved that sentiment analysis methods based on deep learning can continuously train and improve new models based on large amounts of data, and apply them to different sentiment analysis tasks. With the explosion of data today, for a new task, it is necessary to start a new training-process, which faces three serious challenges. First, a great model relies on an annotated corpus, but a corpus that is correctly labeled is difficult to obtain. Manually labeling data is a time-consuming and laborious process. Secondly, the training of the new model has higher performance of the computer, but most ordinary users do not have strong computing power, which limits people's research on higher-level models. Third, people want to develop a unique solution for different individual needs tasks, but according to the above, this process is time consuming and difficult.Therefore, it is more expected that existing models have better generalization ability and can be applied to more different fields. It is precisely because of the above problems VOLUME 7, 2019 that the sentiment analysis method combined with transfer learning has emerged.