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• W4313263102 abstract "Currently, the internet is increasingly popular. More people are used to sharing their feelings about various things on the internet. Online product marketing information is also growing. How to mine the required information from the massive information with the support of big data technology has become a big problem. Thereby, based on the text mining of online product marketing information, this work discusses the text preprocessing methods and the temporal convolution network (TCN) based on a convolutional neural network (CNN). Moreover, on this basis, multimodal attention mechanism (AM) and cross-modal transformer structure are added to build a TCN based on AM (AM-TCN) model to analyze the multimodal emotion of online product marketing information. The results show that the accuracy of the AM-TCN model is 2.88% higher than that of the TCN model alone, and F1 is 3.47% higher. Moreover, the accuracy rate of the AM-TCN is 1.22% higher than that of the next highest recurrent multistage fusion network, and the F1 value is 0.95% higher." @default.
• W4313263102 created "2023-01-06" @default.
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• W4313263102 date "2022-12-29" @default.
• W4313263102 modified "2023-10-16" @default.
• W4313263102 title "The Multimodal Sentiment Analysis of Online Product Marketing Information Using Text Mining and Big Data" @default.
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• W4313263102 doi "https://doi.org/10.4018/joeuc.316124" @default.
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