基于三角形特征融合与感知 注意力的方面级情感分析

doi:10.19678/j.issn.1000-3428.0070397

摘要/Abstract

摘要： 方面级情感分析旨在获取句法结构复杂的语句中特定方面词的情感极性。现有基于依存树与图神经网络的模型因难以完整提取句法结构与深层语义特征，以及特征融合机制有效融合语义特征与句法结构难，导致情感分析中句子情感极限判断的准确率低。为此，建立基于DeBERTa的新型方面级情感分析模型。首先，借助DeBERTa生成文本词向量，同时利用方面感知注意力机制提取方面词的特征，以及利用抽象语义表示获取文本句法结构，减少特征信息因提取不完整而对后续情感分析的影响；其次，构建融合句法结构与深层语义特征的新型三角形乘法机制；最后，经由三角形自注意力机制和全连接网络将方面词的情感极性特征映射到情感分类层，使无关噪声的干扰能得到有效抑制，提升情感极性判断的准确率。11种模型参与求解5种数据集的实验结果表明，相较于最新的基线模型，所获模型的情感极性判断准确率和宏平均F1值分别平均提升0.93%和1.39%，因此该模型更能有效获取句子的句法结构与深层语义，且情感极性的分类准确率高。

Abstract: Aspect-based sentiment analysis aims to analyze the sentiment polarity of specific aspect terms in a sentence. The aspect-based sentiment analysis models, related the existing dependency trees and graph neural networks, easily encounters the low accuracy of sentiment polarity detection of specific aspect terms in a sentence, since not only the syntactic structures and deep semantic features cannot be well extracted, but also such models don’t include effective feature fusion mechanisms. Hereby, the current work develops a new-type and DeBERTa-related aspect-based sentiment analysis model named DeBERTa-ABSA. First, DeBERTa and an aspect-aware attention mechanism are exploited to generate word embedding vectors of the text and extract aspect term features, respectively. Second, the abstract meaning representation (AMR) is chosen to capture the syntactic structure of the text, to ensure that the subsequent sentiment analysis is not influenced by the current feature extraction incompleteness. Third, a new-type triangular multiplication mechanism is introduced to merge syntactic structures and deep semantic features. Finally, a triangular self-attention mechanism and a fully connected network map the sentiment polarity features of the aspect terms to the sentiment classification layer, to effectively avoid the interference of irrelevant noise and promote the accuracy of sentiment polarity detection.

彭李湘松, 张著洪. 基于三角形特征融合与感知注意力的方面级情感分析[J]. 计算机工程, doi: 10.19678/j.issn.1000-3428.0070397.

PENG-LI Xiangsong, ZHANG Zhuhong. Aspect-Based Sentiment Analysis Based on Triangular Feature Fusion and Perceptual Attention[J]. Computer Engineering, doi: 10.19678/j.issn.1000-3428.0070397.

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