Name
Abstract | ||
|---|---|---|
Recently sentiment analysis in Arabic has attracted much attention from researchers. A modest number of studies have been conducted on Arabic sentiment analysis. However, due to the vast increase in users' comments and reviews on social media and e-commerce websites, the necessity to detect sentence-level and aspect-level sentiments has also increased. The aspect-based sentiment analysis has emerged to detect sentiments at the aspect level. Few studies have attempted to perform aspect-based sentiment analysis on Arabic texts because Arabic natural language processing is a challenging task and because of the lack of available Arabic annotated corpora. In this paper, we conducted a systematic review of the methods, techniques, and datasets employed in aspect-based sentiment analysis on Arabic texts. A total of 21 articles published between 2015-2021 were included in this review. After analysing these articles, we found a lack of annotated datasets that can be used by researchers. In addition, the used datasets were limited to few fields. This review will serve as a foundation for researchers interested in Aspect-Based Sentiment Analysis, it will assist them in developing new models and techniques to tackle this task in the future. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ACCESS.2021.3127140 | IEEE ACCESS |
Keywords | DocType | Volume |
Sentiment analysis, Integrated circuits, Task analysis, Hidden Markov models, Systematics, Licenses, Feature extraction, Arabic sentiment analysis, aspect-based sentiment analysis, feature-based sentiment analysis, multi-aspect sentiment analysis, sentiment analysis | Journal | 9 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruba Obiedat | 1 | 0 | 1.35 |
| Duha Al-Darras | 2 | 0 | 0.34 |
| Esra Alzaghoul | 3 | 0 | 0.34 |
| Osama Harfoushi | 4 | 0 | 1.01 |