Research on the Effect of Text Emotion Classification Based on E-commerce Comments
Mining the emotional tendency of analyzing comment texts has become one of the research hotspots in the field of natural language processing in recent years.This paper takes the research perspective of mining the value of commodity comment data in Jingdong Mall,takes the recurrent neural network in deep learning as the theoretical basis,applies the variant models of the recurrent neural network to the text emotion classification task,and compares the comment text classification effect of different improved models.This paper first studies the classification effect of the long and short-term memory model LSTM and the gating loop unit model GRU of the recurrent neural network on the JD commodity review text.The Experiments show that the GRU model has higher accuracy and reaches the optimization value earlier during training,and the GRU net-work model is better than the LSTM network model in text classification.Secondly,the attention neural net-work model driven by emotion words and based on each variant model of the recurrent neural network was studied,combining each deep neural network model with the attention mechanism,and the emotion classifica-tion effect of each combined model was compared and analyzed.The experiment shows that the neural network model introducing the attention mechanism can improve the classification accuracy of the traditional network model,and will reach the optimization value faster.
万方数据
维普
