The DOI matches its Crossref record; 227 of the 227 references it cites were found in a registry or an index.
Systematic reviews in sentiment analysis: a tertiary study
Open accessAbstract
With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.
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Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research
Tan, K. L., Lee, C. P., & Lim, K. M. (2023) · Applied Sciences · doi:10.3390/app13074550 · Cited by 303
Sentiment analysis and opinion mining on educational data: A survey
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2022.100003 · Cited by 215
Echo chamber effects on short video platforms
Gao, Y., Liu, F., & Gao, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-33370-1 · Cited by 109
The inordinate unpopularity of changing all eponymous bird and other organismal names
WINKER, K. (2024) · Bionomina · doi:10.11646/bionomina.37.1.3 · Cited by 5
Metadata
- Authors
- Ligthart, A., Catal, C., & Tekinerdogan, B.
- Venue
- Artificial Intelligence Review
- Published
- 2021-03-03
- Volume
- 54
- Issue
- 7
- Pages
- 4997-5053
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3133966466
- Cited by
- 303
- References
- 227
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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