SEMANTIC SENTIMENT ANALYSIS METHOD FUSING IN-DEPTH FEATURES AND TIME SEQUENCE MODELS

Disclosed is a semantic sentiment analysis method fusing in-depth features and time sequence models, including: converting a text into a uniformly formatted matrix of word vectors; extracting local semantic emotional text features and contextual semantic emotional text features from the matrix of word vectors; weighting the local semantic emotional text features and the contextual semantic emotional text features by using an attention mechanism to generate fused semantic emotional text features; connecting the local semantic emotional text features, the contextual semantic emotional text features and the fused semantic emotional text features to generate global semantic emotional text features; and performing final text emotional semantic analysis and recognition by using a softmax classifier and taking the global semantic emotional text features as input.