Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (5): 140-153.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.05.012

doi: 10.11871/jfdc.issn.2096-742X.2023.05.012

• Technology and Application • Previous Articles     Next Articles

Multi-Level Data Augmentation Method for Aspect-Based Sentiment Analysis

ZHANG Rong1,*(),LIU Yuan2   

  1. 1. School of Internet of Things Engineering, JiangSu Vocational College of Information Technology, WuXi, JiangSu 214153, China
    2. School of Artificial Intelligence and Computer, JiangNan University, WuXi, JiangSu 214122, China
  • Received:2023-03-17 Online:2023-10-20 Published:2023-10-31

Abstract:

[Objective] Aspect-level sentiment analysis provides better insights into user reviews and has become a research hotspot in recent years. This paper designs a simple and effective triple-level data augmentation method, addressing the problem that label data is difficult to obtain in the field of aspect-level sentiment analysis. [Methods] Under the premise of not changing the emotional polarity, sentence-level adjacent words, domain-level similar words, and word vector-level synonyms are replaced for specific target aspects in a comment, which not only ensures label invariance but also generates diverse Synthetic training samples. Each enhancement method in the multi-level data enhancement method can be used either individually or in random combinations. [Results] The proposed schemes are applied to the attention mechanism with the pre-trained model and the dependency tree with the pre-trained model respectively, and tested in the contrastive learning framework. The experiments are carried out on SemEval 2014 Task 4 Sub Task 2. The experimental results show that the proposed data enhancement method is effective, and the values of indicators of Accuracy and Macro-f1 are better than the baseline ones. [Conclusions] Multi-level data augmentation method can effectively alleviate the problem of insufficient data in aspect-level sentiment analysis tasks. It can be used as an effective supplement to the original training data for joint training, and can also be constructed for contrastive learning to implement multi-task training.

Key words: aspect-based sentiment analysis, pre-trained model, data augmentation, dependency parse tree, attention mechanism, contrastive learning