Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape
Sentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resource...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9961195/ |
| _version_ | 1797900215788765184 |
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| author | Koena Ronny Mabokela Turgay Celik Mpho Raborife |
| author_facet | Koena Ronny Mabokela Turgay Celik Mpho Raborife |
| author_sort | Koena Ronny Mabokela |
| collection | DOAJ |
| description | Sentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis techniques for under-resourced languages and the use of high-resourced languages to develop resources for low-resource languages. The ultimate goal is to identify appropriate strategies for future investigations. We report over 35 studies with different languages demonstrating an interest in developing models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for sentiment analysis. Our focus is to critically compare methods, employed datasets and identify research gaps. This study contributes to theoretical literature reviews with complete coverage of multilingual sentiment analysis studies from 2008 to date. Furthermore, we demonstrate how sentiment analysis studies have grown tremendously. Finally, because most studies propose methods based on deep learning approaches, we offer a deep learning framework for multilingual sentiment analysis that does not rely on the machine translation system. According to the meta-analysis protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the sentiment analysis performance. Therefore, deep learning methods are recommended for the development of multilingual sentiment analysis for under-resourced languages. |
| first_indexed | 2024-04-10T08:42:29Z |
| format | Article |
| id | doaj.art-502546672bca4367888a1d7c9a813840 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T08:42:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-502546672bca4367888a1d7c9a8138402023-02-23T00:00:25ZengIEEEIEEE Access2169-35362023-01-0111159961602010.1109/ACCESS.2022.32241369961195Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the LandscapeKoena Ronny Mabokela0https://orcid.org/0000-0002-8058-969XTurgay Celik1https://orcid.org/0000-0001-6925-6010Mpho Raborife2Department of Applied Information Systems, University of Johannesburg, Johannesburg, South AfricaSchool of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South AfricaInstitute for Intelligence Systems, University of Johannesburg, Johannesburg, South AfricaSentiment analysis automatically evaluates people’s opinions of products or services. It is an emerging research area with promising advancements in high-resource languages such as Indo-European languages (e.g. English). However, the same cannot be said for languages with limited resources. In this study, we evaluate multilingual sentiment analysis techniques for under-resourced languages and the use of high-resourced languages to develop resources for low-resource languages. The ultimate goal is to identify appropriate strategies for future investigations. We report over 35 studies with different languages demonstrating an interest in developing models for under-resourced languages in a multilingual context. Furthermore, we illustrate the drawbacks of each strategy used for sentiment analysis. Our focus is to critically compare methods, employed datasets and identify research gaps. This study contributes to theoretical literature reviews with complete coverage of multilingual sentiment analysis studies from 2008 to date. Furthermore, we demonstrate how sentiment analysis studies have grown tremendously. Finally, because most studies propose methods based on deep learning approaches, we offer a deep learning framework for multilingual sentiment analysis that does not rely on the machine translation system. According to the meta-analysis protocol of this literature review, we found that, in general, just over 60% of the studies have used deep learning frameworks, which significantly improved the sentiment analysis performance. Therefore, deep learning methods are recommended for the development of multilingual sentiment analysis for under-resourced languages.https://ieeexplore.ieee.org/document/9961195/Multilingualsentiment analysiscode-switchingdeep learningcross-lingualunderresourced languages |
| spellingShingle | Koena Ronny Mabokela Turgay Celik Mpho Raborife Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape IEEE Access Multilingual sentiment analysis code-switching deep learning cross-lingual underresourced languages |
| title | Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape |
| title_full | Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape |
| title_fullStr | Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape |
| title_full_unstemmed | Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape |
| title_short | Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the Landscape |
| title_sort | multilingual sentiment analysis for under resourced languages a systematic review of the landscape |
| topic | Multilingual sentiment analysis code-switching deep learning cross-lingual underresourced languages |
| url | https://ieeexplore.ieee.org/document/9961195/ |
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