Deep neural networkbased classification model for Sentiment Analysis The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, BiLSTM and CNN were established to judge the tendency of the useru0027s implicit sentiment text. Based on the BiLSTM model, the classification model of wordlevel attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The BiLSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.

TreeStructured Regional CNNLSTM Model for Dimensional Sentiment Analysis Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valencearousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide a more finegrained sentiment analysis. This article proposes a treestructured regional CNNLSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses a part of the text as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and longdistance dependencies across sentences can be considered in the prediction process. To further improve performance, a region division strategy is proposed to discover taskrelevant phrases and clauses to incorporate structured information into VA prediction. Experimental results on different corpora show that the proposed method outperforms lexicon, regression, conventional NN and other structured NN methods proposed in previous studies.