# Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis

arXiv: https://arxiv.org/abs/2006.05602 · HTML: https://mlsanity.com/paper/2006.05602/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Cross-domain Activity Recognition via Substructural Optimal Transport](https://mlsanity.com/paper/2102.03353/) (2021)
- [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://mlsanity.com/paper/2210.02952/) (2022)
- [Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport](https://mlsanity.com/paper/2402.18411/) (2024)
- [Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation](https://mlsanity.com/paper/2107.01598/) (2021)
- [A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis](https://mlsanity.com/paper/2201.07317/) (2022)
- [Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis](https://mlsanity.com/paper/2402.16090/) (2024)
- [AAPA: Adversarially Anchored Preference Alignment for Post-Training of Large Language Models](https://mlsanity.com/paper/2509.25148/) (2025)
- [Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective](https://mlsanity.com/paper/2604.18460/) (2026)
- [Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection](https://mlsanity.com/paper/2209.08681/) (2022)
- [Scaling Unsupervised Multi-Source Federated Domain Adaptation through Group-Wise Discrepancy Minimization](https://mlsanity.com/paper/2510.08150/) (2025)

## Abstract (arXiv)

Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information. Existing algorithms of MS-UDA either only exploit the shared features, i.e., the domain-invariant information, or based on some weak assumption in NLP, e.g., smoothness assumption. To avoid these problems, we propose two transfer learning frameworks based on the multi-source domain adaptation methodology for SA by combining the source hypotheses to derive a good target hypothesis. The key feature of the first framework is a novel Weighting Scheme based Unsupervised Domain Adaptation framework (WS-UDA), which combine the source classifiers to acquire pseudo labels for target instances directly. While the second framework is a Two-Stage Training based Unsupervised Domain Adaptation framework (2ST-UDA), which further exploits these pseudo labels to train a target private extractor. Importantly, the weights assigned to each source classifier are based on the relations between target instances and source domains, which measured by a discriminator through the adversarial training. Furthermore, through the same discriminator, we also fulfill the separation of shared features and private features. Experimental results on two SA datasets demonstrate the promising performance of our frameworks, which outperforms unsupervised state-of-the-art competitors.