  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.

Information Retrieval