# Data Selection Strategies for Multi-Domain Sentiment Analysis

arXiv: https://arxiv.org/abs/1702.02426 · HTML: https://mlsanity.com/paper/1702.02426/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 11 of 11)

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- [Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models](https://mlsanity.com/paper/2309.10707/) (2023)
- [Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes](https://mlsanity.com/paper/2209.09485/) (2022)
- [Generative Deduplication For Socia Media Data Selection](https://mlsanity.com/paper/2401.05883/) (2024)
- [Multi-Domain Targeted Sentiment Analysis](https://mlsanity.com/paper/2205.03804/) (2022)
- [Characterizing and Measuring Linguistic Dataset Drift](https://mlsanity.com/paper/2305.17127/) (2023)
- [A microservice-based framework for exploring data selection in cross-building knowledge transfer](https://mlsanity.com/paper/2102.12970/) (2021)
- [Can We Reliably Rank Model Performance across Domains without Labeled Data?](https://mlsanity.com/paper/2510.09519/) (2025)

## Abstract (arXiv)

Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake -- to our knowledge for the first time -- an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two large-scale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus.