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Currently, there are two main limitations in single-label text classification tasks based on hierarchical graph attention networks. First, it cannot effectively extract text features. Second, there are few studies that highlight text features through the connection between text and labels. To address these two issues, a hierarchical graph attention network text classification model that integrates label information is proposed. The model constructs an adjacency matrix based on the relevance between sentence keywords and topics, and then uses word-level graph attention network to obtain vector representations of sentences. This method is based on randomly initialized target vectors and utilizes maximum pooling to extract specific target vectors for sentences, making the obtained sentence vectors have more obvious category features. After the word-level graph attention layer, a sentence-level graph attention network is used to obtain new text representations with word weight information, and pooling layers are used to obtain feature information for the text. On the other hand, GloVe pre-trained word vectors are used to initialize vector representations for all text labels, which are then interacted and fused with the feature information of the text to reduce the loss of original features, obtaining feature representations that are distinct from different texts. Experimental results on five public datasets (R52, R8, 20NG, Ohsumed, and MR) show that the classification accuracy of the model significantly exceeds other mainstream baseline models. [ABSTRACT FROM AUTHOR]

In the aspect-level sentiment analysis task, the attention mechanism is commonly used to obtain the weight information of words, ignoring the effect of syntactic structure on extracting the importance of different words in sentences. In addition, there is a problem of confusion between aspect words and sentiment words in multi-aspect word sentences. How to effectively pay attention to the context part related to the emotional polarity of the target aspect words is often ignored. A graph attention neural network model (GW-GAT) that integrates graph walk information is proposed. Graph walk is performed on the grammar graph to obtain the word weight coefficient of the sentence. The graph attention network is used to combine the word node weight and the weight between the nodes to highlight the context part that plays an important role in the word sen-timent polarity in the target aspect. Finally, the sentiment polarity is obtained through fully connected and softmax layers. The experimental results on the SemEval2014 task and the Twitter dataset show that the GW-GAT model outperforms the baseline model and obtains better experimental results. [ABSTRACT FROM AUTHOR]

There are quite a few comment sentences without emotional words in aspect-level emotional texts, and the study of their emotions is called aspect-level implicit sentiment analysis. The existing models have the problems that the context information related to aspect words may be lost in the pre-training process, and the deep features in the context cannot be accurately extracted. Aiming at the first problem, this paper constructs an aspect-aware BERT pre-training model, and introduces aspect words into the input embedding structure of basic BERT to generate word vectors related to aspect words. Aiming at the second problem, this paper constructs a context-aware attention mechanism. For the deep hidden vectors obtained from the coding layer, the semantic and syntactic information is introduced into the attention weight calculation process, so that the attention mechanism can more accurately assign weights to the context related to aspect words. The results of comparative experiments show that the proposed model outperforms the baseline model. BERT [ABSTRACT FROM AUTHOR]