## 2 Related Works and Contribution
### Text Sentiment Analysis Methods  
As a hot topic of text analysis, text sentiment analysis has been widely concerned and studied by scholars at home and abroad. While text features are gradually developingfrom TF-IDF and other text statistical features to word vector representation, machine learning and deep learning methods are also applied to this field [28]. Compared with traditional sentiment dictionary methods, machine learning and deep learning methods are characterized by simple data processing, high efficiency, and strong universality. As many large corpora can be used for pre-training, there is no need to worry about the inaccuracy caused by the thesaurus scope [29]. The idea of applying machine learning methods to text sentiment analysis is to use text data with emotion labels to train machine learning models, which can realize sentiment analysis of text in test sets after potential rules are obtained. [PERSON] et al. used the dictionary method and Bayesian algorithm to achieve the emotional classification of medical documents and found that the Bayesian network can reach 80% classification accuracy [30]. [PERSON] et al. proposed a new semi-supervised learning framework based on SVM and found that this method has an excellent performance in text sentiment analysis, and has significantly improved performance compared with general dictionary methods and machine learning methods [31]. In order to recognize semantics emotion terms and emojis, [PERSON] proposed a new sentiment classification framework [32]. The method combined the dictionary methods with the word embedding method and reached satisfactory results. Many other machine learning methods, such as KNN and DT, have also been applied in the field of text sentiment analysis. However, since most emotion text data do not have emotion labels, such supervised learning algorithms are greatly limited in the preparation of training sets [33].  
In recent years, deep learning methods have been continuously applied in various fields. Deep learning methods focus on deep feature mining and can realize text emotion analysis combined with attention mechanism [34], which has great advantages in the text emotion analysis. [PERSON] et al. analyzed the performance of CNN and RNN when applied to Arabic subjective sentiment analysis and verified their capacity in natural language processing (NLP) [35]. [PERSON] et al. used a multi-layer graph convolutional network to recognize text emotions, and set different window sizes at each layer to ensure that the network could learn local and global features, thus improving the emotion recognition effectiveness of the graph convolutional network [36]. [PERSON] et al. verified the effectiveness of CNN and LSTM in text sentiment analysis and found that the recognition accuracy of the deep learning model was higher than that of the general machine learning model [37]. However, different deep networks have their own characteristics. In the face of complex text emotion information, specific deep networks can only dig local deep features. For example, CNN focuses on local semantic feature extraction, but its pool operation will disrupt the text order, which is not conducive to temporal feature analysis [38]. LSTM can learn certain temporal features, but it is not in-depth enough to analyze local text features [39]. Therefore, this paper adopts ensemble learning to integrate different deep network emotion recognition models to improve the overall accuracy and stability of the model.