Research on evaluation and prediction method of consumer satisfaction based on convolutional neural networks

Jia Li

doi:10.1117/12.3004020

11 October 2023 Research on evaluation and prediction method of consumer satisfaction based on convolutional neural networks

Jia Li

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003C (2023) https://doi.org/10.1117/12.3004020
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Perceived value of knowledge-based payment plays a decisive role in consumer behavior and decision-making. Studying the "black box" effect mechanism of perceived value of knowledge-based payment on consumer purchasing intention can help optimize marketing strategies of knowledge payment platforms. In this paper, we propose a method for evaluating and predicting consumer satisfaction based on Convolutional Neural Networks (CNNs). Firstly, we collected and preprocessed a large amount of consumer review data and transformed it into a form that could be processed by our CNN model. Secondly, we designed a CNN model that combined convolutional layers, pooling layers, and fully connected layers, and trained it using backpropagation algorithm. Finally, we used this model to evaluate and predict new consumer review data. Experimental results showed that our method effectively identified sentiment information in reviews, accurately evaluated user satisfaction, and predicted future satisfaction trends with relatively high precision. This proposed method can provide a more reliable means of evaluating and predicting consumer satisfaction for businesses, and has the potential for widespread application in the commercial sector.

Citation Download Citation

Jia Li "Research on evaluation and prediction method of consumer satisfaction based on convolutional neural networks", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003C (11 October 2023); https://doi.org/10.1117/12.3004020

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available