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

• Published in: Data Science and Engineering Vol. 7; no. 3; pp. 279 - 299
• Main Authors: Xu, Yuemei, Cao, Han, Du, Wanze, Wang, Wenqing
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.09.2022; Springer; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s41019-022-00187-3

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.