Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification

TL;DR

This paper introduces a novel emoji-based representation learning approach that enhances cross-lingual sentiment classification by capturing language-specific sentiment patterns, achieving state-of-the-art results especially with limited labeled data.

Contribution

It proposes using emojis as a new channel for learning sentiment-aware representations, improving cross-lingual transfer and handling low-resource scenarios.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively captures language-specific sentiment patterns.

Maintains high accuracy with scarce sentiment labels.

Abstract

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.