Multi Text Classification Model Based on BRET-CNN-BiLSTM

Xu, ErZhuo; Qin, Donghong; Huang, Jun; Zhang, Jinbo

doi:10.1109/bdai56143.2022.9862653

Cited by 4 publications

(1 citation statement)

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“…Most of it revolves around cross-modal mapping between data. As the fields of computer vision and natural language processing continue to evolve, and as more large-scale datasets become available for research, multimodal data mapping methods continue to mature [37]. The mainstream multimodal data mapping method is: based on the existing mapping relationship, the existing multimodal data is first symbolized or vectorized, which is used as the input to the neural network, and then combined with the existing correspondences, mapped to another modal.…”

Section: Relevant Researchmentioning

confidence: 99%

Research on Online Review Information Classification Based on Multimodal Deep Learning

Liu,

Sun,

Zhang

et al. 2024

Preprint

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The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers' emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves highly applicable to online reviews.

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Section: Relevant Researchmentioning

confidence: 99%