# Cross-Lingual Sentiment Analysis Without (Good) Translation

arXiv: https://arxiv.org/abs/1707.01626 · HTML: https://mlsanity.com/paper/1707.01626/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Compositional Generalization in Multilingual Semantic Parsing over Wikidata](https://mlsanity.com/paper/2108.03509/) (2021)
- [Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon](https://mlsanity.com/paper/2402.02113/) (2024)
- [Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study](https://mlsanity.com/paper/2106.10719/) (2021)
- [Multi-task Learning for Cross-Lingual Sentiment Analysis](https://mlsanity.com/paper/2212.07160/) (2022)
- [Linear Transformations for Cross-lingual Sentiment Analysis](https://mlsanity.com/paper/2209.07244/) (2022)
- [Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis](https://mlsanity.com/paper/2409.20054/) (2024)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)

## Abstract (arXiv)

Current approaches to cross-lingual sentiment analysis try to leverage the wealth of labeled English data using bilingual lexicons, bilingual vector space embeddings, or machine translation systems. Here we show that it is possible to use a single linear transformation, with as few as 2000 word pairs, to capture fine-grained sentiment relationships between words in a cross-lingual setting. We apply these cross-lingual sentiment models to a diverse set of tasks to demonstrate their functionality in a non-English context. By effectively leveraging English sentiment knowledge without the need for accurate translation, we can analyze and extract features from other languages with scarce data at a very low cost, thus making sentiment and related analyses for many languages inexpensive.