Research on capsule network-based for aspect-level sentiment classification

Zhidong XU; Bingyang CHEN; Xiao WANG; Weishan ZHANG

doi:10.11959/j.issn.2096-6652.202031

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Research on capsule network-based for aspect-level sentiment classification

Regular Papers | 更新时间：2024-06-05

- Research on capsule network-based for aspect-level sentiment classification
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 3, Pages: 284-292(2020)
- 作者机构：
  
  1. 中国人民解放军国防大学国家安全学院，北京100091
  2. 中国石油大学（华东）计算机科学与技术学院，山东青岛 266580
  3. 青岛智能产业技术研究院，山东青岛 266109
  4. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京100190
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202031
  CLC： TP301
- Published Online：2020-09，
  
  Published：20 September 2020
- 稿件说明：
移动端阅览
Zhidong XU, Bingyang CHEN, Xiao WANG, et al. Research on capsule network-based for aspect-level sentiment classification[J]. Chinese journal of intelligent science and technology, 2020, 2(3): 284-292.
DOI：

Zhidong XU, Bingyang CHEN, Xiao WANG, et al. Research on capsule network-based for aspect-level sentiment classification[J]. Chinese journal of intelligent science and technology, 2020, 2(3): 284-292. DOI： 10.11959/j.issn.2096-6652.202031.

摘要

由于文本中多种情感极性混合而难以判断，方面级情感分析成为当前研究的热点。考虑到多面句表达时会在一定程度上对不同目标的多重情感造成特征重叠，进而影响文本情感分类效果，提出一种基于胶囊网络的方面级情感分类模型（SCACaps）。模型使用序列卷积分别提取上下文和方面词的特征，同时引入交互注意力机制，减少二者对彼此的影响，并对文本特征表示进行重构后传入胶囊网络。胶囊层间通过引入高层胶囊系数对路由算法进行优化，整个迭代更新过程的参数全局共享，以保存较完整的文本特征信息。通过与多个模型进行对比实验发现，SCACaps的分类效果最佳，同时，在小样本学习中SCACaps也有较好的表现。

Abstract

Considering the difficulty of judging the mixed multiple sentimental polarities in a text

aspect-level sentiment analysis has become a hot research topic.Multiple sentiments of different targets when expressing multi-faceted in a sentence

it will cause problems such as feature overlap

which will have a negative impact on text sentiment classification.A capsule network-based model for aspect-level sentiment classification (SCACaps) was proposed.Sequential convolution was used to extract the features of context and aspect words separately

and an interactive attention mechanism was introduced to reduce the mutual influence on each other

and then the text feature representation was transmitted into the capsule network after reconstruction.The routing algorithm was optimized by introducing high-level capsule coefficients between the capsule layers

and the global parameters were shared in the entire iterative update process to save relatively complete text feature information.By comparing with multiple models

the SCACaps model has the best classification effect

and the SCACaps model also performs better in small sample learning.

关键词

Keywords

references

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