Emotion recognition of social media users based on deep learning

Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, “joy,” “anger,” and “sadness,” are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) model, emotion dictionary, and manual annotation is created to extract emotional feature words. The captured motivational text is converted into a word vector using Word2vec. After gathering the long-distance semantic data with bidirectional long short-term memories (BiLSTM) and convolutional neural networks (CNN) extract the text’s key characteristics to finish the emotion categorization. The test results demonstrate an average increase in F1 value of 3.66 percent for six machine learning models and an average increase in F1 value of 1.84 percent for seven deep learning models. The suggested model performs better at identifying the emotions of social media users than the current machine learning and deep learning methods.