Abstract
Perceived value is the customer’s subjective understanding of the value they obtain and is their subjective evaluation of the product or service they enjoy. This value is deducted from the cost of the product or service. In order to understand and predict the specific cognition of consumers on the value of products or services and distinguish it from the objective value of products or services in the general sense, this paper uses the in-depth learning method based on LSTM to establish a model to predict the perceived benefits of consumers. It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms. This paper proposes a new short-text representation method based on bidirectional LSTM. This method is very effective for forecasting research. In addition, we also use the attention mechanism to learn the specific emotional vocabulary. Short-text representation can be used for emotion classification and emotion intensity prediction. This paper evaluates the proposed classification model and regression data set. Compared with the baseline of the corresponding data set, the contrast of the results was 93%. The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived utility of products to consumers.
Highlights
Online shopping is a brand new shopping experience, which is a kind of e-commerce
BiLSTM is a stack of two long short-term memory (LSTM): forward processing information from t 1 − T, while reverse processing information from t T−1. e equations of the LSTM layer remain unchanged, and the random gradient can be used for training
BiLSTM is used to train, develop, and test the data set of consumer reviews, and an improved BiLSTM recurrent neural network system is proposed to better control how much information each memory unit outputs. e forgetting gate and input gate are combined into a single update gate. e BiLSTM network is a brnns using the LSTM hidden layer
Summary
Online shopping is a brand new shopping experience, which is a kind of e-commerce Consumers use their mobile terminal devices to enter the online shopping platform to buy the goods and services they want. E intention of travel consumption in chat robots refers to the willingness of users to purchase products or services in order to meet their travel needs. E author suggests that the intention of consumer products should be determined to enhance user experience. E author takes the task of consumer intention recognition as a classification problem and combines the deep learning method to identify. The author uses the convolutional long short-term memory (LSTM) neural network model to identify travel consumption intention. Word-based systems cannot effectively learn these expressions, which affects the performance of consumer intention recognition or perceived value classification. The innovation of this paper is to propose the use of the attention mechanism to improve the efficiency of the learning system, which proves that the proposed combination is superior to the state-ofthe-art perceptual utility classification mechanism
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