# Linear Transformations for Cross-lingual Sentiment Analysis

arXiv: https://arxiv.org/abs/2209.07244 · HTML: https://mlsanity.com/paper/2209.07244/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://mlsanity.com/paper/1911.03437/) (2019)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data](https://mlsanity.com/paper/1806.04511/) (2018)
- [Cross-Lingual Word Embeddings for Turkic Languages](https://mlsanity.com/paper/2005.08340/) (2020)
- [Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings](https://mlsanity.com/paper/1602.02373/) (2016)
- [Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation](https://mlsanity.com/paper/2010.03486/) (2020)
- [Cross-Lingual Sentiment Analysis Without (Good) Translation](https://mlsanity.com/paper/1707.01626/) (2017)
- [Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across Languages](https://mlsanity.com/paper/1805.09016/) (2018)
- [Are the Multilingual Models Better? Improving Czech Sentiment with Transformers](https://mlsanity.com/paper/2108.10640/) (2021)
- [Linear Transformations for Cross-lingual Semantic Textual Similarity](https://mlsanity.com/paper/1807.04172/) (2018)

## Built on by (most cited first, 2 of 2)

- [Prompt-Based Approach for Czech Sentiment Analysis](https://mlsanity.com/paper/2508.08651/) (2025)
- [Cross-lingual Aspect-Based Sentiment Analysis: A Survey on Tasks, Approaches, and Challenges](https://mlsanity.com/paper/2508.09516/) (2025)

## Abstract (arXiv)

This paper deals with cross-lingual sentiment analysis in Czech, English and French languages. We perform zero-shot cross-lingual classification using five linear transformations combined with LSTM and CNN based classifiers. We compare the performance of the individual transformations, and in addition, we confront the transformation-based approach with existing state-of-the-art BERT-like models. We show that the pre-trained embeddings from the target domain are crucial to improving the cross-lingual classification results, unlike in the monolingual classification, where the effect is not so distinctive.