用于情感分析的注意力感知型长短期记忆类尖峰神经模型

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
International Journal of Neural Systems Pub Date : 2023-08-01 Epub Date: 2023-06-10 DOI:10.1142/S0129065723500375
Qian Liu, Yanping Huang, Qian Yang, Hong Peng, Jun Wang
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引用次数: 0

摘要

LSTM-SNP 模型是最近开发的一种长短期记忆(LSTM)网络,其灵感来自尖峰神经 P(SNP)系统的机制。本文利用 LSTM-SNP 提出了一种用于方面情感分析的新型模型,称为 ALS 模型。LSTM-SNP 模型有三个门:重置门、消耗门和生成门。此外,LSTM-SNP 模型还集成了注意力机制。ALS 模型能更好地捕捉文本中的情感特征,计算上下文和方面词之间的相关性。为了验证 ALS 模型在方面情感分析中的有效性,我们在三个真实数据集上与 17 个基准模型进行了对比实验。实验结果表明,与这些基线模型相比,ALS 模型结构更简单,性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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