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:
Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v1
Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints 2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v1
Ihsan, U.; Ashraf, H.; Jhanjhi, N. Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints2023, 2023121990. https://doi.org/10.20944/preprints202312.1990.v1
APA Style
Ihsan, U., Ashraf, H., & Jhanjhi, N. (2023). Multilingual Sentiment Analysis Using Deep Learning: Survey. Preprints. https://doi.org/10.20944/preprints202312.1990.v1
Chicago/Turabian Style
Ihsan, U., Humaira Ashraf and NZ Jhanjhi. 2023 "Multilingual Sentiment Analysis Using Deep Learning: Survey" Preprints. https://doi.org/10.20944/preprints202312.1990.v1
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.
