Article: Emotion recognition of consumer comments based on graphic fusion Journal: International Journal of Information and Communication Technology (IJICT) 2024 Vol.25 No.12 pp.46 - 65 Abstract: Multimodal sentiment classification is the use of image and text data to mine the potential emotions in consumer reviews. The picture scenes of the comments are complex, and there is a lot of information affecting the emotional judgement. To solve this problem, we propose a multimodal sentiment analysis model based on image translation to eliminate the redundant features of images and improve the efficiency of the model. Enhance text data to generate diverse data. Interactive learning realises the deep fusion of image and text. Leverage transformer to convert images into text by inputting them into space and output accurate consumer sentiment information in the images. Construct auxiliary sentences through translation, increase the amount of available text, and output classification results. Experiments were carried out on multi-ZOL, Twitter-2015 and 2017 datasets, and the accuracy rate reached 71.94%, 79% and 88.35%, respectively, which is far superior to other advanced methods. Inderscience Publishers - linking academia, business and industry through research

Title: Emotion recognition of consumer comments based on graphic fusion

Authors: Li Lin; Minglan Yuan; Shangzhen Pang; Rongping Wang

Addresses: Finance and Economics Department, Nanchong Vocational and Technical College, Nanchong, Sichuan 637000, China; Graduate School of Business (GSB), SEGi University, Kota Damansara 47810, Malaysia ' School of Economic Management and Public Affairs, Chongqing Vocational College of Architecture and Engineering, Chongqing 400072, China ' School of Physics and Electronic Engineering, Sichuan University of Science and Engineering, Zigong, Sichuan 643000, China ' School of Finance and Trade Management, Chengdu Vocational and Technical College of Industry and Trade, Chengdu, Sichuan 611731, China

Abstract: Multimodal sentiment classification is the use of image and text data to mine the potential emotions in consumer reviews. The picture scenes of the comments are complex, and there is a lot of information affecting the emotional judgement. To solve this problem, we propose a multimodal sentiment analysis model based on image translation to eliminate the redundant features of images and improve the efficiency of the model. Enhance text data to generate diverse data. Interactive learning realises the deep fusion of image and text. Leverage transformer to convert images into text by inputting them into space and output accurate consumer sentiment information in the images. Construct auxiliary sentences through translation, increase the amount of available text, and output classification results. Experiments were carried out on multi-ZOL, Twitter-2015 and 2017 datasets, and the accuracy rate reached 71.94%, 79% and 88.35%, respectively, which is far superior to other advanced methods.

Keywords: emotion analysis; picture translation; transformer; consumer reviews; text enhancement.

DOI: 10.1504/IJICT.2024.143630

International Journal of Information and Communication Technology, 2024 Vol.25 No.12, pp.46 - 65

Received: 24 Oct 2024
Accepted: 21 Nov 2024
Published online: 02 Jan 2025 *

Title: Emotion recognition of consumer comments based on graphic fusion

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