Document - Cross-Lingual Sentiment Quantification

2020

Journal article Open Access

Cross-Lingual Sentiment Quantification

Esuli A., Moreo A., Sebastiani F.

Computer Science - Machine Learning Machine Learning (stat.ML) Statistics - Machine Learning quantification sentiment analysis Artificial Intelligence Computer Networks and Communications cross-lingual Information Retrieval (cs.IR) quanet FOS: Computer and information sciences Computer Science - Information Retrieval Machine Learning (cs.LG)

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.

Source: IEEE intelligent systems 35 (2020): 106–113. doi:10.1109/MIS.2020.2979203

Publisher: IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America

Citations

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BibTeX entry

@article{oai:it.cnr:prodotti:438786,
	title = {Cross-Lingual Sentiment Quantification},
	author = {Esuli A. and Moreo A. and Sebastiani F.},
	publisher = {IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America},
	doi = {10.1109/mis.2020.2979203 and 10.48550/arxiv.1904.07965},
	journal = {IEEE intelligent systems},
	volume = {35},
	pages = {106–113},
	year = {2020}
}