Journal
IEEE INTELLIGENT SYSTEMS
Volume 35, Issue 3, Pages 106-113Publisher
IEEE COMPUTER SOC
DOI: 10.1109/MIS.2020.2979203
Keywords
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Funding
- SoBigdata++ project - European Commission under Programme H2020INFRAIA-2019-1 [871042]
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Sentiment Quantification is the task of estimating the relative frequency of sentiment-related classes-such as Positive and 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 S, but not for the target language 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 crosslingual sentiment classification show that the presented method performs cross-lingual sentiment quantification with high accuracy.
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