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.

中文翻译：

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跨语言情绪量化

情感量化是在一组未标记的文档中估计与情感相关的类别（例如 ${\sf Positive}$Positive 和 ${\sf Negative}$Negative）的相对频率的任务。它是情感分析中的一个重要主题，因为对整个群体中与情感相关的数量和趋势的研究通常比对单个实例的分析更受关注。在这篇文章中，我们提出了一种跨语言情感量化的方法，当训练文档可用于源语言 $\mathcal {S}$S，但不适用于目标语言 $\mathcal {T 时，执行情感量化的任务}$T，需要对其进行情感量化。跨语言情感量化（以及一般的跨语言文本量化）以前从未在文献中讨论过；我们通过将最先进的量化方法与能够生成所涉及的源文档和目标文档的跨语言矢量表示的方法相结合，为二进制案例建立基线结果。在用于跨语言情感分类的公开可用数据集上的实验表明，所提出的方法以高精度执行跨语言情感量化。