# Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

arXiv: https://arxiv.org/abs/1806.02557 · HTML: https://mlsanity.com/paper/1806.02557/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 9 of 9)

- [An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applications](https://mlsanity.com/paper/2101.04930/) (2021)
- [Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge](https://mlsanity.com/paper/2104.02704/) (2021)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Cross-lingual Capsule Network for Hate Speech Detection in Social Media](https://mlsanity.com/paper/2108.03089/) (2021)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [Contrastive Learning-based Multi Modal Architecture for Emoticon Prediction by Employing Image-Text Pairs](https://mlsanity.com/paper/2408.02571/) (2024)
- [Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training](https://mlsanity.com/paper/2409.14552/) (2024)
- [The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://mlsanity.com/paper/2506.00583/) (2025)
- [YNU-HPCC at SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Using Multiple Prediction Headers](https://mlsanity.com/paper/2609.19238/) (2026)

## Abstract (arXiv)

Sentiment classification typically relies on a large amount of labeled data. In practice, the availability of labels is highly imbalanced among different languages, e.g., more English texts are labeled than texts in any other languages, which creates a considerable inequality in the quality of related information services received by users speaking different languages. To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i.e., the source language, usually English) to another language with fewer labels (i.e., the target language). The source and the target languages are usually bridged through off-the-shelf machine translation tools. Through such a channel, cross-language sentiment patterns can be successfully learned from English and transferred into the target languages. This approach, however, often fails to capture sentiment knowledge specific to the target language, and thus compromises the accuracy of the downstream classification task. In this paper, we employ emojis, which are widely available in many languages, as a new channel to learn both the cross-language and the language-specific sentiment patterns. We propose a novel representation learning method that uses emoji prediction as an instrument to learn respective sentiment-aware representations for each language. The learned representations are then integrated to facilitate cross-lingual sentiment classification. The proposed method demonstrates state-of-the-art performance on benchmark datasets, which is sustained even when sentiment labels are scarce.