# Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

arXiv: https://arxiv.org/abs/1910.13425 · HTML: https://mlsanity.com/paper/1910.13425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach](https://mlsanity.com/paper/2105.03983/) (2021)

## Abstract (arXiv)

The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.