A Hybrid Neural Network Architecture for Semantic-Contextual Analysis of Emotions in Social Media

ENG: The paper proposes a hybrid neural network architecture for multi-class classification of the emotional state of social media texts, which combines contextual vector representations generated by the BERT model with extended structured features related to the user and message metadata. The proposed approach uses a multilayer perceptron network that combines linguistic and contextual information in a single representation. The results of the experimental study confirm the superiority of the proposed approach over traditional LSTM and CNN architectures, as well as over the separate use of BERT embeddings. The achieved classification accuracy is 90%, and the F1-measure is 0.91, which indicates the high efficiency of the model in conditions of high variability of language structures and stylistic features of social content.