Sentiment Analysis And User Behavior Research Based On Online Reviews

With the rapid development of e-commerce industry,consumers pay more and more attention to product quality and shopping experience.In order to facilitate businesses and logistics companies to listen to consumers' opinions,e-commerce platforms usually have the function of product review.Product reviews include consumers' evaluation of product quality,logistics efficiency and platform usage.Businesses,logistics companies and e-commerce platforms can plan development strategies and improve service quality according to product reviews.Due to the abstractness and complexity of text data and the colloquial and networked features of product reviews,the effect of sentiment analysis based on rule-based sentiment dictionary or machine learning is not ideal.In order to improve the ability of emotion orientation recognition of the model,this dissertation proposes Text Inception and two fusion models based on convolutional neural networks(CNNs),recurrent neural networks(RNNs)and Self-Attention mechanism.1.On the basis of text convolution neural network(TextCNN).This dissertation proposes Text Inception which is a text convolution model based on the idea of the structure design of the Inception model in the field of image processing.2.Based on RNNs,multi self attention,sentence embedding and Text Inception,SA-Bi LSTM-Text Inception fusion model is designed.Multi-Self-Attention is used to enhance the context information of each input node.The second feature extraction and output direction are realized by SA-Bi LSTM and Text Inception.Add layer combines two sentence vectors to serve as the output result of SA-Bi LSTM-Text Inception.3.Based on the transfer learning method of ELMo,this dissertation proposes SA-ELMo-Text Inception fusion model.Using ELMo to produce word vectors can significantly enhance the feature extraction ability of the model and make the model understand more complex syntactic and semantic information.The experimental results are analyzed after the pre-processing of the commodity review data training,and the experimental results are analyzed to verify the improvement of the classification ability of ELMo feature extraction and focal loss function.