TY  - JOUR
Y1  - 2024///
A1  - Aliyu, Y.
A1  - Sarlan, A.
A1  - Usman Danyaro, K.
A1  - Rahman, A.S.B.A.
A1  - Abdullahi, M.
JF  - IEEE Access
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192996918&doi=10.1109%2fACCESS.2024.3398635&partnerID=40&md5=103a06d33f5d7d5f3129768f5a7bfb57
VL  - 12
N2  - The field of low-resource sentiment analysis has seen significant developments in recent years. This research review SLR evaluates the approaches and data sources utilized in low-resource sentiment analysis by deep learning. The primary aim is to discover suitable approaches for future sentiment analysis in low-resource. Our studies explore various languages, models, and data sources expressing a desire to create effective approaches. Our emphasis lies in the critical evaluation of the approaches and the datasets utilized, to identify areas where further research is needed. Our analysis study adds to the existing body of literature reviews, encompassing multilingual low-resource sentiment analysis research spanning from 2018 to 2023. The findings indicate that the transfer learning approach is the most frequently used, followed by word embedding learning and machine translation systems. Additionally, the study shows that social media is the most used platform for data collection, followed by product reviews, movies, and hotels. There has been a significant surge in the adoption of pre-trained transformers, indicating a growing interest in exploring the potential of these models for low-resource languages within the natural language processing (NLP) community. This trend is largely attributed to the novel nature of these models and their feature of being non-labour intensive. However, the scarcity of annotated datasets for such languages remains a major hurdle. finally, these research findings are relevant and informative for any researcher working in the field of low-resource multilingual sentiment analysis. The study introduces a conceptual framework for performing sentiment analysis in low-resource. The study provides a valuable resource for future researchers. © 2013 IEEE.
ID  - scholars20011
KW  - Computational linguistics; Computer aided language translation; Data mining; Deep learning; Machine translation
KW  -  Data-source; Embeddings; Low-resource; Low-resource settings; Machine translations; Multilingual; Sentiment analysis; Transfer learning; Transformer; Word-embedding
KW  -  Sentiment analysis
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 21693536
EP  - 66909
AV  - none
N1  - cited By 0
TI  - Sentiment Analysis in Low-Resource Settings: A Comprehensive Review of Approaches, Languages, and Data Sources
SP  - 66883
ER  -