A Systematic Review of Cross-Lingual Sentiment Analysis: Tasks, Strategies, and Prospects

被引：15

作者：

Zhao, Chuanjun ^{[1
]}

Wu, Meiling ^{[1
]}

Yang, Xinyi ^{[1
]}

Zhang, Wenyue ^{[1
]}

Zhang, Shaoxia ^{[1
]}

Wang, Suge ^{[2
]}

Li, Deyu ^{[2
]}

机构：

[1] Shanxi Univ Finance & Econ, Shanxi Key Lab Econ Big Data, 140 Wucheng Rd, Taiyuan 030000, Peoples R China

[2] Shanxi Univ, Key Lab Computat Intelligence & Chinese Informat, Minist Educ, 92 Wucheng Rd, Taiyuan 030000, Peoples R China

来源：

ACM COMPUTING SURVEYS | 2024年 / 56卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Cross-lingual sentiment analysis; coarse; and fine-grained sentiment analysis; machine translation; sentiment transfer strategy; summary research; CLASSIFICATION; MODEL; TRANSLATION;

D O I：

10.1145/3645106

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Traditional methods for sentiment analysis, when applied in a monolingual context, often yield less than optimal results in multilingual settings. This underscores the need for a more thorough exploration of cross-lingual sentiment analysis (CLSA) methodologies to improve analytical effectiveness. CLSA, confronted with obstacles such as linguistic disparities and a lack of resources, seeks to evaluate sentiments across a range of languages. First, the research background, challenges, existing solution ideas, and evaluation tasks of CLSA are summarized. Subsequently, new perspectives including different granularity levels, machine translation support, and sentiment transfer strategies perspectives are highlighted. Finally, potential avenues for future research are discussed.

引用

页数：37

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