Cross-domain emotion classification system based on attention mechanism fusion

The invention relates to a cross-domain emotion classification system based on attention mechanism fusion. The system comprises a comment text preprocessing module used for obtaining vector forms of texts in a source domain and a target domain; a text semantic learning module which is used for learning a semantic dependency relationship between words; an attention mechanism fusion module which isused for fusing different attention modes to obtain comprehensive weights of words for text classification; a hierarchical attention module which is used for calculating attention weights of the textfrom a word level and a sentence level respectively and judging weights of words for sentence representation and sentences for document representation; and an emotion category output module which is used for obtaining a final emotion classification result by utilizing the classification function. According to the method, the potential universal features of the target domain and the source domain can be automatically extrac