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| Cross-Lingual Sentiment Analysis: A Survey |
Xu Yuemei( ),Cao Han,Wang Wenqing,Du Wanze,Xu Chengyang |
| School of Information Science and Technology, Beijing Foreign Studies of University, Beijing 100089, China |
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Abstract [Objective] This paper teases out the research context of cross-lingual sentiment analysis (CLSA). [Coverage] We searched “TS=cross lingual sentiment OR cross lingual word embedding” in Web of Science database and 90 representative papers were chosen for this review. [Methods] We elaborated the following CLSA 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. [Results] We analyzed their main ideas, methodologies, shortcomings, etc., and attempted to reach a conclusion on the coverage of languages, datasets and their performance. It is found that although pre-trained models such as Multi-BERT have achieved good performance in zero-shot cross-lingual sentiment analysis, some challenges like language sensitivity still exist. Early CLSA methods still have some inspirations for existing researches. [Limitations] Some CLSA models are mixed models and they are classified according to the main methods. [Conclusions] We look into the future development of CLSA and the challenges facing the research area. With in-depth research of pre-trained models on multi-lingual semantics, CLSA models fit for more and wider languages will be the future direction.
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Received: 11 May 2022
Published: 16 February 2023
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| Fund:Fundamental Research Funds for the Central Universities(2022JJ006) |
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Corresponding Authors:
Xu Yuemei，ORCID：0000-0002-0223-7146，E-mail： xuyuemei@bfsu.edu.cn。
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