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Lifelong Learning for Sentiment Classification

arXiv 1801.02808 ↗First posted 9 Jan 2018cs.CL cs.IR cs.LGcited by 21

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Abstract (arXiv)

This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from past tasks and use it to help future learning. In this paper, we first discuss LL in general and then LL for sentiment classification in particular. The proposed LL approach adopts a Bayesian optimization framework based on stochastic gradient descent. Our experimental results show that the proposed method outperforms baseline methods significantly, which demonstrates that lifelong learning is a promising research direction.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 9; 2023: 0; 2024: 1; 2025: 2; 2026: 1.

21 papers.

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  1. 2021Achieving Forgetting Prevention and Knowledge Transfer in Continual Learningcited by 91
  2. 2021Continual Learning of a Mixed Sequence of Similar and Dissimilar Taskscited by 80
  3. 2022A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challengescited by 61
  4. 2021Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Taskscited by 46
  5. 2022Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Taskcited by 35
  6. 2022Graph Lifelong Learning: A Surveycited by 30
  7. 2021CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Taskscited by 29
  8. 2022ConTinTin: Continual Learning from Task Instructionscited by 26
  9. 2021Deep Bayesian Unsupervised Lifelong Learningcited by 21
  10. 2021Continual Learning with Knowledge Transfer for Sentiment Classificationcited by 12
  11. 2021Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classificationcited by 7
  12. 2022Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threatscited by 7
9 more
  1. 2021On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Modelscited by 6
  2. 2022Zero-Shot Aspect-Based Sentiment Analysiscited by 5
  3. 2022Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillationcited by 5
  4. 2022RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learningcited by 4
  5. 2022Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Languagecited by 3
  6. 2024HOP to the Next Tasks and Domains for Continual Learning in NLPcited by 3
  7. 2025Deontic Temporal Logic for Formal Verification of AI Ethicscited by 2
  8. 2025Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learningcited by 0
  9. 2026Detection of Illicit Content on Online Marketplaces using Large Language Modelscited by 0