A combination of TEXTCNN model and Bayesian classifier for microblog sentiment analysis

More and more individuals are paying attention to the research on the emotional information found in micro-blog comments. TEXTCNN is growing rapidly in the short text space. However, because the training model of TEXTCNN model itself is not very extensible and interpretable, it is difficult to quantify and evaluate the relative importance of features and themselves. At the same time, word embedding can't solve the problem of polysemy at one time. This research suggests a microblog sentiment analysis method based on TEXTCNN and Bayes that addresses this flaw. First, the word embedding vector is obtained by word2vec tool, and based on the word vector, the ELMo word vector integrating contextual features and different semantic features is generated by ELMo model. Second, the local features of ELMo word vector are extracted from multiple angles by using the convolution layer and pooling layer of TEXTCNN model. Finally, the training task of emotion data classification is completed by combining Bayes classifier. On the Stanford Sentiment Classification Corpus data set SST (Stanford Sentiment Classification Corpus Data bank), the experimental findings demonstrate that the model in this paper is compared with TEXTCNN, LSTM, and LSTM–TEXTCNN models. The Accuracy, Precision, Recall, and F1-score of the experimental results of this research have all greatly increased. Their values are respectively 0.9813, 0.9821, 0.9804 and 0.9812, which are superior to other comparison models and can be effectively used for emotional accurate analysis and identification of events in microblog emotion analysis.