Emotion analysis method based on emotion intensity fusion and BiGRU

Abstract

In Chinese sentiment analysis, sentiment words are just a drop in the ocean compared with the whole corpus. In order to solve the problem of insufficient emotion lexicon and prior knowledge, proposes a method to predict the emotion intensity of target words based on neural network model (Neural Network Emebdding Score, NNES). By training a small number of labeled samples, using clustering algorithm to find the seed words, calculate the similarity between the target words and the seed words, and using it as the input of neural network to predict the emotional intensity of the unlabeled words. Compared with the traditional machine learning regression models, it has smaller mean square error. Meanwhile, a BiGRU model based on attention mechanism and convolution is proposed by integrating the predicted emotion intensity with word vector (Neural Network Emebdding Score with CNN and Attention-BiGRU, NNESC-Att-BiGRU). To compare several popular models on product and hotel review data sets, and the proposed model has better classification effect on Chinese sentiment classification task.