DACNN: Dynamic Weighted Attention with Multi-channel Convolutional Neural Network for Emotion Recognition

In recent years, due to the vigorous development of social network media, a large amount of social data can be obtained through social media. This trend has made it easier for researchers in natural language processing to obtain textual research materials, and has further stimulated the development of natural language processing. Emotion recognition is an important task in the field of natural language processing, and emotion recognition helps improve interaction in social. In this study, the emotion recognition of the content of twitter users' posts and comments is based on the sentence level. We use large datasets to collect users' posts and comments on Twitter for comparative analysis, and we attempt to understand the emotions expressed by users. Most of the recent studies focus on the analysis of vocabulary and syntactic features in sentences to learn the emotion of the sentence. However, the property of multi-feature may be conducive for promoting the performance of the model and may also be a disturbance of the training of the model. Our study is based on the latest of attention mechanism and XLNet to improve the recognition of emotions. We propose a novel emotion recognition model called Dynamic Weighted Attention with Multichannel Convolutional Neural Network (DACNN), which combines the multi-channel convolutional neural network and the attention mechanism of automatic weight adjustment to effectively improve the outcome of emotion recognition. In addition, we use the latest word embedding technology XLNet to obtain high-quality feature vectors of sentences. In the experiments, we compare the DACNN with the baselines on various datasets, and the results show that DACNN is better than the state-of-the-art methods in accuracy, precision, recall, and fl-score. The experimental results also prove that our attention mechanism and dynamic weighting can well promote the accuracy of emotion recognition.