Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media
Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation meth...
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| Veröffentlicht in: | Journal of organizational and end user computing 2022-01, Vol.34 (8), p.1-18 |
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| creator | Zeng, Xiao Zhong, Ziqi |
| description | Currently, with the dramatic increase in social media users and the greater variety of online product information, manual processing of this information is time-consuming and labour-intensive. Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. This research has important practical significance for MSA of online product information in social media. |
| doi_str_mv | 10.4018/JOEUC.314786 |
| format | Article |
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Therefore, based on the text mining of online information, this paper analyzes the text representation method of online information, discusses the long short-term memory network, and constructs an interactive attention graph convolutional network (IAGCN) model based on graph convolutional neural network (GCNN) and attention mechanism to study the multimodal sentiment analysis (MSA) of online product information. The results show that the IAGCN model improves the accuracy by 4.78% and the F1 value by 29.25% compared with the pure interactive attention network. Meanwhile, it is found that the performance of the model is optimal when the GCNN is two layers and uses syntactic position attention. 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| subjects | Artificial neural networks Data mining Data warehousing/data mining Neural network Neural networks Sentiment analysis Social media User statistics |
| title | Multimodal Sentiment Analysis of Online Product Information Based on Text Mining Under the Influence of Social Media |
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