Cross-Lingual Sentiment Analysis: A Survey

被引：0
|
作者：
Xu Y. [1 ]
Cao H. [1 ]
Wang W. [1 ]
Du W. [1 ]
Xu C. [1 ]
机构：
[1] School of Information Science and Technology, Beijing Foreign Studies of University, Beijing
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 01期
关键词：
Bilingual Word Embedding; Cross Lingual; Multi-lingual; Sentiment Analysis;
D O I：
10.11925/infotech.2096-3467.2022.0472
中图分类号：
学科分类号：
摘要：
[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. © 2023, Chinese Academy of Sciences. All rights reserved.
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页码：1 / 21
页数：20
相关论文
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