Aspect-level sentiment analysis model fused with GPT and multi-layer attention

Lai Mingzheng; Hong Zelin; Lu Jiadong; Lv Wei

doi:10.1117/12.2671363

28 April 2023 Aspect-level sentiment analysis model fused with GPT and multi-layer attention

Lai Mingzheng, Hong Zelin, Lu Jiadong, Lv Wei

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261018 (2023) https://doi.org/10.1117/12.2671363
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

With the popularization of the Internet and the rapid development of artificial intelligence, the requirements for the accuracy of sentiment analysis on text data have been continuously improved, and aspect-level sentiment analysis has emerged as the times require. This paper analyzes and combines the research results of aspect-level sentiment analysis in recent years and proposes an aspect-level sentiment analysis model integrating GPT and multi-layer attention. (GPT and Multi-Layer Attention Network, GPT-MAN). The model first combines GPT and aspect coding to obtain a new word vector representation method, and then inputs the word vector with rich semantics into the network with a multi-layer attention mechanism, Sentiment Analysis Accuracy. In the experiment, the GPT-MAN model is analyzed from the selection of word embedding model and the optimization of hyperparameters using the datasets of Restaurant, Laptop and Twitter, and the effectiveness of this method is verified. And in comparison, with different models, the accuracy of the model in the three datasets increased by 2.57, 3.34, and 1.19 percentage points, respectively.

Citation Download Citation

Lai Mingzheng, Hong Zelin, Lu Jiadong, and Lv Wei "Aspect-level sentiment analysis model fused with GPT and multi-layer attention", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261018 (28 April 2023); https://doi.org/10.1117/12.2671363

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