A Survey of Cross-lingual Sentiment Analysis: Methodologies, Models and Evaluations

Cross-lingual sentiment analysis (CLSA) leverages one or several source languages to help the low-resource languages to perform sentiment analysis. Therefore, the problem of lack of annotated corpora in many non-English languages can be alleviated. Along with the development of economic globalizatio...

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Vydané v:Data Science and Engineering Ročník 7; číslo 3; s. 279 - 299
Hlavní autori: Xu, Yuemei, Cao, Han, Du, Wanze, Wang, Wenqing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Nature Singapore 01.09.2022
Springer
Springer Nature B.V
Predmet:
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Chemistry and Earth Sciences
Computer Science
Data mining
Data Mining and Knowledge Discovery
Database Management
Datasets
Global economy
Globalization
Languages
Machine translation
Non-English languages
Physics
Sentiment analysis
State-of-the-art reviews
Statistics for Engineering
Survey Paper
Surveys
Systems and Data Security
United Kingdom
Japan
China
Bilingual word embedding
Cross-lingual
Sentiment analysis
ISSN:2364-1185, 2364-1541
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Shrnutí:Cross-lingual sentiment analysis (CLSA) leverages one or several source languages to help the low-resource languages to perform sentiment analysis. Therefore, the problem of lack of annotated corpora in many non-English languages can be alleviated. Along with the development of economic globalization, CLSA has attracted much attention in the field of sentiment analysis and the last decade has seen a surge of researches in this area. Numerous methods, datasets and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper fills the gap by reviewing the state-of-the-art CLSA approaches from 2004 to the present. This paper teases out the research context of cross-lingual sentiment analysis and elaborates the following methods in detail: (1) The early main methods of CLSA, including those based on Machine Translation and its improved variants, parallel corpora or bilingual sentiment lexicon; (2) CLSA based on cross-lingual word embedding; (3) CLSA based on multi-BERT and other pre-trained models. We further analyze their main ideas, methodologies, shortcomings, etc., and attempt to reach a conclusion on the coverage of languages, datasets and their performance. Finally, we look into the future development of CLSA and the challenges facing the research area.
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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-022-00187-3