# Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification

arXiv: https://arxiv.org/abs/1908.09122 · HTML: https://mlsanity.com/paper/1908.09122/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Reproducibility, Replicability and Beyond: Assessing Production Readiness of Aspect Based Sentiment Analysis in the Wild](https://mlsanity.com/paper/2101.09449/) (2021)
- [WIDER & CLOSER: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2212.03506/) (2022)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [Topic Driven Adaptive Network for Cross-Domain Sentiment Classification](https://mlsanity.com/paper/2111.14094/) (2021)
- [Similarity-Based Domain Adaptation with LLMs](https://mlsanity.com/paper/2503.05281/) (2025)

## Abstract (arXiv)

Cross-domain sentiment classification has drawn much attention in recent years. Most existing approaches focus on learning domain-invariant representations in both the source and target domains, while few of them pay attention to the domain-specific information. Despite the non-transferability of the domain-specific information, simultaneously learning domain-dependent representations can facilitate the learning of domain-invariant representations. In this paper, we focus on aspect-level cross-domain sentiment classification, and propose to distill the domain-invariant sentiment features with the help of an orthogonal domain-dependent task, i.e. aspect detection, which is built on the aspects varying widely in different domains. We conduct extensive experiments on three public datasets and the experimental results demonstrate the effectiveness of our method.