ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (9): 2003-2014.doi: 10.7544/issn1000-1239.20210699

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Weakly-Supervised Contrastive Learning Framework for Few-Shot Sentiment Classification Tasks

Lu Shaoshuai1, Chen Long1, Lu Guangyue1, Guan Ziyu2, Xie Fei3   

  1. 1(School of Communications and Information Engineering, Xi’an University of Posts & Telecommunications, Xi’an 710121);2(School of Computer Science and Technology, Xidian University, Xi’an 710071);3(Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071)
  • Online:2022-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61936006, 61876144, 61876145, 62103314), the Key Research and Development Program of Shaanxi (2021ZDLGY02-06), and the Natural Science Basic Research Program of Shaanxi (2020JQ-850).

Abstract: Text sentiment classification is a challenge research topic in natural language processing. Lexicon-based methods and traditional machine learning-based methods rely on high-quality sentiment lexicon and robust feature engineering respectively, whereas most deep learning methods are heavily reliant on large human-annotated data sets. Fortunately, users on various social platforms generate massive amounts of tagged opinioned texts which can be deemed as weakly-labeled data for sentiment classification. However, noisy labeled instances in weakly-labeled data have a negative impact on the training phase. In this paper, we present a weakly-supervised contrastive learning framework for few-shot sentiment classification that learns the sentiment semantics from large user-tagged data with noisy labels while also exploiting inter-class contrastive patterns hidden in small labeled data. The framework consists of two steps: first, we design a weakly-supervised pre-training strategy to reduce the influence of the noisy labeled samples, and then the contrastive strategy is used in supervised fine-tuning to capture the contrast patterns in the small labeled data. The experimental results on Amazon review data set show that our approach outperforms the other baseline methods. When fine-tuned on only 0.5% (i.e. 32 samples) of the labels, we achieve comparable performance among the deep baselines, showing its robustness in the data sparsity scenario.

Key words: sentiment classification, weakly-supervised learning, supervised contrastive learning, few-shot learning, transfer learning

CLC Number: