Cross-Lingual Sentiment Quantification
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We discuss Cross-Lingual Text Quantification (CLTQ), the task of performing text quantification (i.e., estimating the relative frequency p_c(D) of all classes c∈C in a set D of unlabelled documents) when training documents are available for a source language S but not for the target language T for which quantification needs to be performed. CLTQ 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. We present experimental results obtained on publicly available datasets for cross-lingual sentiment classification; the results show that the presented methods can perform CLTQ with a surprising level of accuracy.
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