An emotion classification method, comprising: acquiring original text data, and performing text preprocessing on the original text data to obtain an initial word set (S1); performing encoding processing on the initial word set to obtain an integer code, and performing vectorization processing on the initial word set according to the integer code to obtain a standard word vector set (S2); performing bidirectional semantic processing on the standard word vector set to obtain a semantic word vector set (S3); performing screening processing on the semantic word vector set using a preset long short-term memory network to obtain a target text sequence, performing probability calculation on the target text sequence according to a preset attention mechanism to obtain a probability value, and analyzing the probability value to obtain an emotion classification result (S4). In addition, the present invention further relates to blockchain technology, and the initial word set can be stored in a node of a blockchain. Further provided are an emotion classification apparatus, an electronic device, and a computer-readable storage medium, capable of solving the problem of low accuracy in emotion classification.
EMOTION CLASSIFICATION METHOD AND APPARATUS, ELECTRONIC DEVICE, AND STORAGE MEDIUM