Liu, J.; Sun, Y.; Zhang, Y.; Lu, C. Research on Online Review Information Classification Based on Multimodal Deep Learning. Appl. Sci.2024, 14, 3801.
Liu, J.; Sun, Y.; Zhang, Y.; Lu, C. Research on Online Review Information Classification Based on Multimodal Deep Learning. Appl. Sci. 2024, 14, 3801.
Liu, J.; Sun, Y.; Zhang, Y.; Lu, C. Research on Online Review Information Classification Based on Multimodal Deep Learning. Appl. Sci.2024, 14, 3801.
Liu, J.; Sun, Y.; Zhang, Y.; Lu, C. Research on Online Review Information Classification Based on Multimodal Deep Learning. Appl. Sci. 2024, 14, 3801.
Abstract
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.
Keywords
Deep learning; Multimodality; Online review information; Sentiment analysis
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
