Version 1
: Received: 25 December 2023 / Approved: 26 December 2023 / Online: 26 December 2023 (11:19:03 CET)
Version 2
: Received: 1 January 2024 / Approved: 3 January 2024 / Online: 3 January 2024 (03:13:11 CET)
How to cite:
Bibi, A.; Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v2
Bibi, A.; Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints 2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v2
Bibi, A.; Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v2
APA Style
Bibi, A., Ihsan, U., Ashraf, H., & Jhanjhi, N. (2024). Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints. https://doi.org/10.20944/preprints202312.1990.v2
Chicago/Turabian Style
Bibi, A., Humaira Ashraf and NZ Jhanjhi. 2024 "Multilingual Sentiment Analysis Using Deep Learning: Survey" Preprints. https://doi.org/10.20944/preprints202312.1990.v2
Abstract
The rise of the Internet has enabled people to express their opinions on various subjects through social media, blogs, and website comments. As a result, there has been a significant increase in research on sentiment analysis. However, most of the research efforts have focused on analyzing sentiment in English-language data, neglecting the wealth of information available in other languages. In this paper, we provide a comprehensive review of the current state-of-the-art in multilingual sentiment analysis. The survey investigates techniques for data preprocessing, representation learning, and feature extraction in multilingual sentiment analysis. It explores cross-lingual transfer learning, domain adaptation, and data augmentation methods that enhance the performance of sentiment analysis models, particularly in low-resource languages and domains. It provides insights into the state-of-the-art approaches, challenges, and opportunities in this evolving field and encourages further advancements in multilingual sentiment analysis research.
Keywords
sentiment; deep learning; multilingual
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
3 January 2024
Commenter:
Humaira Ashraf
Commenter's Conflict of Interests:
Author
Comment:
Dear Sir, Good day, This paper has been updated by the newly added author, such as figures 1, 3, and 5, and the Introduction section has been updated mainly besides other changes. This work represents the same group/research lab of research scholars, and initially, by mistake, one of the authors was missed. It is kindly requested to consider the changes, and upload version 2, please. Thank you. Best Regards Dr Humaira
Commenter: Humaira Ashraf
Commenter's Conflict of Interests: Author
Good day,
This paper has been updated by the newly added author, such as figures 1, 3, and 5, and the Introduction section has been updated mainly besides other changes. This work represents the same group/research lab of research scholars, and initially, by mistake, one of the authors was missed. It is kindly requested to consider the changes, and upload version 2, please.
Thank you.
Best Regards
Dr Humaira