Cross-Lingual Sentiment Quantification

Sentiment Quantification is the task of estimating the relative frequency of sentiment-related classes—such as ${\sf Positive}$Positive and ${\sf Negative}$Negative—in a set of unlabeled documents. It is an important topic in sentiment analysis, as the study of sentiment-related quantities and trends across a population is often of higher interest than the analysis of individual instances. In this article, we propose a method for cross-lingual sentiment quantification, the task of performing sentiment quantification when training documents are available for a source language $\mathcal {S}$S, but not for the target language $\mathcal {T}$T, for which sentiment quantification needs to be performed. Cross-lingual sentiment quantification (and cross-lingual text quantification in general) has never been discussed before in the literature; we establish baseline results for the binary case by combining state-of-the-art quantification methods with methods capable of generating cross-lingual vectorial representations of the source and target documents involved. Experiments on publicly available datasets for cross-lingual sentiment classification show that the presented method performs cross-lingual sentiment quantification with high accuracy.