An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis

LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three g...

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Bibliographic Details
Published inInternational journal of neural systems Vol. 33; no. 8; p. 2350037
Main Authors Liu, Qian, Huang, Yanping, Yang, Qian, Peng, Hong, Wang, Jun
Format Journal Article
LanguageEnglish
Published Singapore 01.08.2023
Subjects
Humans
Neural Networks, Computer
Sentiment Analysis
Sentiment analysis
LSTM-SNP model
attention mechanism
membrane computing
nonlinear spiking neural P systems
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Summary:LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with LSTM-SNP model. The ALS model can better capture the sentiment features in the text to compute the correlation between context and aspect words. To validate the effectiveness of the ALS model for aspect-level sentiment analysis, comparison experiments with 17 baseline models are conducted on three real-life data sets. The experimental results demonstrate that the ALS model has a simpler structure and can achieve better performance compared to these baseline models.
ISSN:1793-6462
DOI:10.1142/S0129065723500375