# Lifelong Learning for Sentiment Classification

arXiv: https://arxiv.org/abs/1801.02808 · HTML: https://mlsanity.com/paper/1801.02808/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2018.

## Built on by (most cited first, 21 of 21)

- [Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2112.02706/) (2021)
- [Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks](https://mlsanity.com/paper/2112.10017/) (2021)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.03271/) (2021)
- [Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task](https://mlsanity.com/paper/2208.12037/) (2022)
- [Graph Lifelong Learning: A Survey](https://mlsanity.com/paper/2202.10688/) (2022)
- [CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks](https://mlsanity.com/paper/2112.02714/) (2021)
- [ConTinTin: Continual Learning from Task Instructions](https://mlsanity.com/paper/2203.08512/) (2022)
- [Deep Bayesian Unsupervised Lifelong Learning](https://mlsanity.com/paper/2106.07035/) (2021)
- [Continual Learning with Knowledge Transfer for Sentiment Classification](https://mlsanity.com/paper/2112.10021/) (2021)
- [Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification](https://mlsanity.com/paper/2106.11197/) (2021)
- [Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats](https://mlsanity.com/paper/2204.03433/) (2022)
- [On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models](https://mlsanity.com/paper/2108.00114/) (2021)
- [Zero-Shot Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2202.01924/) (2022)
- [Life-long Learning for Multilingual Neural Machine Translation with Knowledge Distillation](https://mlsanity.com/paper/2212.02800/) (2022)
- [RVAE-LAMOL: Residual Variational Autoencoder to Enhance Lifelong Language Learning](https://mlsanity.com/paper/2205.10857/) (2022)
- [Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language](https://mlsanity.com/paper/2205.01204/) (2022)
- [HOP to the Next Tasks and Domains for Continual Learning in NLP](https://mlsanity.com/paper/2402.18449/) (2024)
- [Deontic Temporal Logic for Formal Verification of AI Ethics](https://mlsanity.com/paper/2501.05765/) (2025)
- [Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning](https://mlsanity.com/paper/2501.12121/) (2025)
- [Detection of Illicit Content on Online Marketplaces using Large Language Models](https://mlsanity.com/paper/2603.04707/) (2026)

## 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.