Paper
8 November 2023 Research on multilingual sentiment analysis based on N-Gram and gating mechanism
Ziqian Cui, Lei Wang
Author Affiliations +
Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292320 (2023) https://doi.org/10.1117/12.3011493
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China
Abstract
The development of the Internet makes online reviews tend to be multilingual. In the study of aspect sentiment analysis tasks, traditional monolingual methods is unable to fully extract the reviews sentiment information in multilingual situations. To solve the limitations of traditional monolingual sentiment analysis in multilingual situations, a ngram-based multilingual gated convolutional neural network model was proposed. The model divided the review text into the left context and the right context, and uses convolutional neural network to obtain ngram features of different size between the left and right contexts. After max pooling, the gating mechanism is used to realize the interaction between ngram features. Finally, the features extracted from the left context and the right context is used to detect aspect categories. In this paper, several experiments have been conducted for the proposed model. The experimental results show that the performance of the proposed model is significantly better than other comparison models, which can more effectively extract the emotional information of user reviews more effectively and improve the accuracy of aspect category detection.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ziqian Cui and Lei Wang "Research on multilingual sentiment analysis based on N-Gram and gating mechanism", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292320 (8 November 2023); https://doi.org/10.1117/12.3011493
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KEYWORDS
Performance modeling

Matrices

Data modeling

Analytical research

Convolutional neural networks

Feature extraction

Neural networks

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