Machine learning and deep learning for sentiment analysis across languages: A survey. Mercha, E. M. & Benbrahim, H. Neurocomputing, 531:195–216, April, 2023. ![Machine learning and deep learning for sentiment analysis across languages: A survey [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The inception and rapid growth of the Web, social media, and other online forums have resulted in the continuous and rapid generation of opinionated textual data. Several real-world applications have been focusing on determining the sentiments expressed in these data. Owing to the multilinguistic nature of the generated data, there exists an increasing need to perform sentiment analysis on data in diverse languages. This study presents an overview of the methods used to perform sentiment analysis across languages. We primarily focus on multilingual and cross-lingual approaches. This survey covers the early approaches and current advancements that employ machine learning and deep learning models. We categorize these methods and techniques and provide new research directions. Our findings reveal that deep learning techniques have been widely used in both approaches and yield the best results. Additionally, the scarcity of multilingual annotated datasets limits the progress of multilingual and cross-lingual sentiment analyses, and therefore increases the complexity in comparing these techniques and determining the ones with the best performance.
@article{mercha_machine_2023,
title = {Machine learning and deep learning for sentiment analysis across languages: {A} survey},
volume = {531},
issn = {0925-2312},
shorttitle = {Machine learning and deep learning for sentiment analysis across languages},
url = {https://www.sciencedirect.com/science/article/pii/S0925231223001546},
doi = {10.1016/j.neucom.2023.02.015},
abstract = {The inception and rapid growth of the Web, social media, and other online forums have resulted in the continuous and rapid generation of opinionated textual data. Several real-world applications have been focusing on determining the sentiments expressed in these data. Owing to the multilinguistic nature of the generated data, there exists an increasing need to perform sentiment analysis on data in diverse languages. This study presents an overview of the methods used to perform sentiment analysis across languages. We primarily focus on multilingual and cross-lingual approaches. This survey covers the early approaches and current advancements that employ machine learning and deep learning models. We categorize these methods and techniques and provide new research directions. Our findings reveal that deep learning techniques have been widely used in both approaches and yield the best results. Additionally, the scarcity of multilingual annotated datasets limits the progress of multilingual and cross-lingual sentiment analyses, and therefore increases the complexity in comparing these techniques and determining the ones with the best performance.},
language = {en},
urldate = {2023-03-21},
journal = {Neurocomputing},
author = {Mercha, El Mahdi and Benbrahim, Houda},
month = apr,
year = {2023},
keywords = {Cross-lingual sentiment analysis, Deep learning, Machine learning, Multilingual sentiment analysis, Sentiment analysis},
pages = {195--216},
}
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