Open Access

ARTICLE

Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

1 School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
2 Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, 430065, China

* Corresponding Author: Leijun Wang. Email:

Computers, Materials & Continua 2024, 80(1), 1581-1599. https://doi.org/10.32604/cmc.2024.052666

Received 10 April 2024; Accepted 14 June 2024; Issue published 18 July 2024

Abstract

In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model is applied for sentiment classification of those keyword-generated images. For method validation, the data randomly comprised of 5000 reviews from Amazon have been analyzed. With superior keyword extraction capability, the proposed method achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%. Such performance demonstrates its advantages by using the text-to-image approach, providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works. Thus, the proposed method enhances the reliability and insights of customer feedback surveys, which would also establish a novel direction in similar cases, such as social media monitoring and market trend research.

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Keywords

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