User emotion analysis method based on multi-model fusion

The invention relates to a new sentiment classification model based on hybrid learning. In the first stage, an improved dictionary classification method is used for calculating emotion scores on a whole data set, and data with extremely high scores or extremely low scores are directly marked; in the second stage, the rest is used to calculate the emotion score based on the emotion dictionary and the BI-GRU fusion model, and the two-stage hybrid framework enables the method to be effectively applied to emotion classification. Experiments show that a single model is unsatisfactory in emotion classification effect under various complex contexts, high in difficulty and low in precision, and error preference of the single model can be effectively improved by adopting a multi-model fusion method, so that the classification effect is improved. 本发明涉及了一种新的基于混合学习的情感分类模型。该发明在第一阶段，在整个数据集上使用改进的字典分类法来计算情感得分，直接标记得分极高或极低的数据；在第二阶段，剩下的采用基于情感词典和BI-GRU融合模型来计算情感得分，两阶段的混合框架使得该方法在情感分类中得到了有效的应用。本发明经实验表明，单一模型对多种复杂语境下的情感分类效果不理想，且难度大、精