## 1 Introduction
In recent years, models that combine character-level and word-level text features have appeared one after another; for example, [PERSON] et al. [7] used CNN (Convolutional Neural Network) for feature extraction of word-level text after completing word text embedding, a Bi-GRU (Bidirectional Gated Recurrent Unit) combined with the attention mechanism for feature extraction of word-level text, and finally fused the two to enhance the effect. [PERSON] [8] et al. adopted dynamic encoding for character-level text and static encoding for word-level text, using a dual-channel network to complete a named entity recognition task, etc. The above studies prove that joint character- and word-level text features can indeed improve model performance, but the dynamic encoding is not used for all features when they are encoded. Although the existing deep learning models have become more comprehensive in obtaining text information, the accuracy of text coding still needs to be improved. Second, the existing models use a combination of CNN and RNN (Recurrent Neural Network) to make full use of the local and global semantic information in the text, which is time-consuming. CNN has excellent local learning ability but cannot obtain global semantic information. RNN can capture the global information of the input sequence, but local key feature acquisition ability is poor. To solve this problem, [PERSON] et al. [9] used multi-channel modeling to combine CNN with Bi-LSTM to improve the effect of text classification in medical records. [PERSON] [10] et al. fused CNN with BiGRU as a way to achieve sentiment analysis of text. Although the above models can obtain both the local and global semantic information in a text, they have a high time cost due to the complex structure of RNN models. Third, existing models have typically adopted pooling strategies to reduce the parameters and speed up model convergence, but the ordinary pooling approach has the drawback of losing key text information. Max-pooling [11] and average-pooling [12] are the two most common pooling approaches in the existing deep learning models. Max-pooling filters the salient features in the text and discards other non-significant features, while average-pooling averages the text features. However, neither the salient nor average features are the most key features of the text. Using the above pooling approaches will result in the loss of some important information, and are not able to achieve dynamic optimization of features. Fourth, existing character-word association feature models mostly use direct splicing or simple summation to integrate character-level and word-level information, making it difficult to achieve effective utilization and fusion of character-word features. [PERSON] et al. [13] and [PERSON] et al. [14] used simple splicing to fuse the word information of text. The word-level information and character-level information contained in a text are complementary to each other, and the importance of both varies in different contexts, so existing methods of combining character-level and word-level information are not appropriate. To improve the effectiveness of microblog sentiment analysis, we propose a microblog sentiment analysis model (DCCMM) based on Dynamic Character and Word Encoding, CNN, Cross-Channel Feature fusion, Multi-Head Self-Attention Pooling, and Multi-Granularity Feature Interaction Fusion. The innovations of this paper are as follows. 1. To cope with the frequent occurrence of multiple meanings and the limited information contained in microblog text, and to solve the problems of the inaccuracy and incompleteness of the information presented by existing text coding methods, we propose a dual-stream dynamic coding method for characters and words, using WOBERT Plus and ALBERT to achieve word-level dynamic embedding and character-level dynamic embedding of text, respectively, so that the model is able to obtain more comprehensive and accurate semantic information. 2.