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.
Cross-Lingual Sentiment Quantification
Esuli A;Sebastiani F
2020
Abstract
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.File | Dimensione | Formato | |
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prod_438786-doc_157399.pdf
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Descrizione: Cross-Lingual Sentiment Quantification
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prod_438786-doc_159213.pdf
Open Access dal 01/02/2021
Descrizione: Cross-Lingual Sentiment Quantification
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2.94 MB
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prod_438786-doc_164187.pdf
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Descrizione: Cross-Lingual Sentiment Quantification
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