Abstract:

Considering the inherent characteristics of the film review context information and the irrationality of the word order, a CRCNN (Conditional Random Field Convolutional Neural Networks) model is proposed for text sentiment analysis. In order to reduce the impact of noise data on the analysis, the convolutional neural network is improved, and a weight distribution layer is introduced between the input layer and the convolutional layer to analyze the important parts, reduce the noise, and improve the processing characteristics. Using the gradient descent method in the convolutional layer to solve the training parameters will cause the gradient to spread or explode. In order to solve this problem, a gating mechanism is introduced. Finally, the sequence label layer is introduced, and the semantic features of neural network learning are optimized. In addition, the word granularity word vector is used as the feature, which solves the segmentation of the ambiguous word while learning more specific features. Experiments show that the model has significantly better film evaluation effect than other models.

Key words: film review, sentiment analysis, convolutional neural network, sequence labeling layer, word granularity word vector