## 2 Related Work
### Deep Learning Methods  
Researchers are devoting an increasing amount of time and energy to the study and development of deep learning and sentiment categorization models that are powered by deep learning. Convolutional neural networks (CNN) play a significant role in computer vision, and the You Only Look Once (YOLO) [15] network model has various applications in target identification. Natural language processing has also benefited from its use. The attention-based convolutional multi-label sentiment classification neural network uses the convolutional neural network to multi-label sentiment classification, and the model separates the attitudes carried by the phrases by replicating the human comprehension process [16]. The convolutional kernel is able to extract the entire utterance's characteristics more fully, and the addition of an attention mechanism further enhances the text sentiment analysis capability. A CNN-based model combines machine learning and convolutional neural networks to detect negative statements in text [17]. This method is comparable to the random forest classifier, and it is concluded that convolutional neural networks are more innovative and promising as classifiers. However, this method can only be applied to a single dimension, namely aggressiveness, and it is difficult to scale up for multicategorical sentiment analysis tasks [18]. This technique makes use of a \"deep\" network; however, the structure of the network is preset according to the amount of the text that is to be processed and is analogous to more conventional forms of mechanical learning. [PERSON] et al. [19] used a convolutional neural network for sentiment analysis and designed a simple convolutional neural network model for benchmarking. This network hasonly one convolutional layer; the text features are extracted block by block through the pooling layer into the fully-connected layer; the dropout operation is added to prevent the occurrence of overfitting; and the final output is binary classification data. Word2 Vec is a neural network model proposed by [PERSON] et al. [20] for training distributed word-embedding representations. This model captures the semantic similarity between words, and this similarity can be expressed in the word vector space. The word vectors are trained by context, and the context is predicted based on the words. When dealing with Chinese natural language tasks, Chinese words are composed of Chinese characters, and there is no space between words for segmentation due to the peculiarity of the Chinese language, which relies on readers' experience and context to recognize words. [PERSON] et al. [21] proposed using the word vector model for learning microblogging text features and proposed the CNN_Text_Word2 vec model in combination with CNN networks. The advantages and disadvantages of the two different coding models, word granularity and letter granularity, were investigated for Chinese text sentiment analysis. The results show that Word2 vec has a significant advantage over word-granularity-trained vectors in Chinese sentiment classification tasks. CNN networks, however, are poor at learning the sequential dimension, which eliminates the lexical sequential features in sentences and reduces recognition accuracy [14]. Recurrent neural networks (RNN) can recognize contextual information on sequences and preserve the sequential features of words; however, due to the gradient disappearance problem, the capture of long-term memory by RNN does not achieve the desired results [22]. Thus, based on RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were proposed to make up for the shortages of RNN and solve the problem of gradient disappearance by selective forgetting. In the realm of emotion separation, LSTM has produced promising results [23]. Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) are, therefore, proposed to acquire contextual information in both directions. However, the network complexity for lengthy texts is quite significant [24]. The work of [PERSON] et al.