A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis

ZHANG Huan-xiang; PENG Jun-jie

doi:10.12263/DZXB.20230037

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A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis

PAPERS | 更新时间：2025-12-24

- A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis
- ACTA ELECTRONICA SINICA Vol. 52, Issue 7, Pages: 2307-2319(2024)
- 作者机构：
  
  1.上海大学计算机工程与科学学院,上海 200444
  2.上海先进通信与数据科学研究院,上海 200444
  3.内蒙古科技大学创新创业教育学院,内蒙古包头 014010
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20230037
  CLC： TP391.1;
- Received：10 January 2023，
  
  Revised：2023-06-30，
  
  Published：25 July 2024
- 稿件说明：
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张换香, 彭俊杰. 基于方面级情感分析的深度语义挖掘模型[J]. 电子学报, 2024, 52(07): 2307-2319.

ZHANG Huan-xiang, PENG Jun-jie. A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis[J]. Acta Electronica Sinica, 2024, 52(07): 2307-2319.
张换香, 彭俊杰. 基于方面级情感分析的深度语义挖掘模型[J]. 电子学报, 2024, 52(07): 2307-2319. DOI：10.12263/DZXB.20230037

ZHANG Huan-xiang, PENG Jun-jie. A Deep Semantic Mining Model Based on Aspect-Level Sentiment Analysis[J]. Acta Electronica Sinica, 2024, 52(07): 2307-2319. DOI：10.12263/DZXB.20230037

摘要

方面级情感分析是一种细粒度的情感分类任务，具有广泛的应用前景，正因为如此，得到了广泛关注与研究，尤其是近年来，基于依赖树的图神经网络和基于注意力的网络模型的研究取得了较大进展.但是，由于在线评论表达的复杂性和依赖关系不易解析使得这些方法在情感分析的性能上得不到有效提升.为了克服这些挑战，本文提出了一种同时考虑句法语义和上下文语义的深度语义挖掘模型（Deep Semantic Mining Model，DSMM）.具体地，为了深度挖掘句法背后隐含的深度语义，模型采用并行的图卷积和多头注意力机制挖掘丰富的语义；为了充分利用句法语义和上下文语义的内在关联关系，采用了关联注意力机制获取句法语义和上下文语义的相关性，并且采用自适应方面路由机制有效获取方面的情感语义，并在此基础上，通过引入基于依赖树的语义位置嵌入，进一步增强方面-意见词的关联.在三个公共数据集上的实验结果表明，该模型在复杂句情感分析中既能从不同语义空间挖掘句子的语义特征，也能有效利用句法特征强化句子的语义表征，在分类准确率和泛化能力上的表现优于相关工作.

Abstract

Aspect level sentiment analysis is a fine-grained sentiment classification task

which has a wide range of application prospects. Therefore

it has been widely concerned and researched

especially in recent years

the graph neural network based on dependency tree and the network model based on attention have made great progress. However

these studies are limited by factors such as the difficulty in parsing dependency and the complex expression of online reviews. To overcome these challenges

this paper proposes a deep semantic mining model (DSMM) that considers both syntactic and contextual semantics. Specifically

in order to mine deep semantic hidden behind the syntax

the model uses parallel graph convolution and multi-head self-attention to mine rich semantic. In order to make full use of the intrinsic correlation between syntactic semantics and contextual semantics

we used the relevance attention mechanism to obtain the correlation between syntactic semantics and contextual semantics

and we used the adaptive aspect routing mechanism to obtain the sentiment semantics of aspects effectively. Moreover

we introduced the semantic location embedding based on dependency tree to further enhance the aspect-opinion word correlation. The experimental results on three public datasets show that our model can not only mine the semantic features of sentences from different semantic spaces

but also effectively use the syntactic features to strengthen the semantic representation of sentences in sentiment analysis of complex sentence

and the performance of classification accuracy and generalization ability is better than that of related work.

关键词

Keywords

references

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