Cargando…

A consumer emotion analysis system based on support vector regression model

The effective means to stimulate economic growth is to enhance consumers’ consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at dif...

Descripción completa

Detalles Bibliográficos
Autores principales: Huo, Mingkui, Li, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280612/
https://www.ncbi.nlm.nih.gov/pubmed/37346521
http://dx.doi.org/10.7717/peerj-cs.1381
_version_ 1785060835043835904
author Huo, Mingkui
Li, Jing
author_facet Huo, Mingkui
Li, Jing
author_sort Huo, Mingkui
collection PubMed
description The effective means to stimulate economic growth is to enhance consumers’ consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at different times. By mining the online comments of consumers on the online fitness platform, we can find the characteristics of fitness projects that consumers care about. Analyzing consumers’ emotional tendencies towards the characteristics of fitness programs will help online fitness platforms adjust the quality and service direction of fitness programs in a timely manner. At the same time, it can also provide purchase advice and suggestions for other consumers. Based on this goal, this study uses an optimized support vector regression (SVR) model to build a consumer sentiment analysis system, so as to predict the consumer’s willingness to pay. The optimized SVR model uses the region convolution neural network (RCNN) to extract features from the dataset, and uses feature data to train the SVR model. The experimental results show that the SVR model optimized by RCNN is more accurate. The improvement of the accuracy of consumer sentiment analysis can accurately help businesses promote and publicize, and increase sales. On the other hand, the increase in the accuracy of emotion analysis can also help users quickly locate their favorite fitness projects, saving browsing time. To sum up, the emotional analysis system for consumers in this paper has good practical value.
format Online
Article
Text
id pubmed-10280612
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher PeerJ Inc.
record_format MEDLINE/PubMed
spelling pubmed-102806122023-06-21 A consumer emotion analysis system based on support vector regression model Huo, Mingkui Li, Jing PeerJ Comput Sci Data Mining and Machine Learning The effective means to stimulate economic growth is to enhance consumers’ consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at different times. By mining the online comments of consumers on the online fitness platform, we can find the characteristics of fitness projects that consumers care about. Analyzing consumers’ emotional tendencies towards the characteristics of fitness programs will help online fitness platforms adjust the quality and service direction of fitness programs in a timely manner. At the same time, it can also provide purchase advice and suggestions for other consumers. Based on this goal, this study uses an optimized support vector regression (SVR) model to build a consumer sentiment analysis system, so as to predict the consumer’s willingness to pay. The optimized SVR model uses the region convolution neural network (RCNN) to extract features from the dataset, and uses feature data to train the SVR model. The experimental results show that the SVR model optimized by RCNN is more accurate. The improvement of the accuracy of consumer sentiment analysis can accurately help businesses promote and publicize, and increase sales. On the other hand, the increase in the accuracy of emotion analysis can also help users quickly locate their favorite fitness projects, saving browsing time. To sum up, the emotional analysis system for consumers in this paper has good practical value. PeerJ Inc. 2023-05-09 /pmc/articles/PMC10280612/ /pubmed/37346521 http://dx.doi.org/10.7717/peerj-cs.1381 Text en ©2023 Huo and Li https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Huo, Mingkui
Li, Jing
A consumer emotion analysis system based on support vector regression model
title A consumer emotion analysis system based on support vector regression model
title_full A consumer emotion analysis system based on support vector regression model
title_fullStr A consumer emotion analysis system based on support vector regression model
title_full_unstemmed A consumer emotion analysis system based on support vector regression model
title_short A consumer emotion analysis system based on support vector regression model
title_sort consumer emotion analysis system based on support vector regression model
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280612/
https://www.ncbi.nlm.nih.gov/pubmed/37346521
http://dx.doi.org/10.7717/peerj-cs.1381
work_keys_str_mv AT huomingkui aconsumeremotionanalysissystembasedonsupportvectorregressionmodel
AT lijing aconsumeremotionanalysissystembasedonsupportvectorregressionmodel
AT huomingkui consumeremotionanalysissystembasedonsupportvectorregressionmodel
AT lijing consumeremotionanalysissystembasedonsupportvectorregressionmodel