The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Sentiment Classification Using a Single-Layered BiLSTM Model
Open accessAbstract
This study presents a computationally efficient deep learning model for binary sentiment classification, which aims to decide the sentiment polarity of people's opinions, attitudes, and emotions expressed in written text. To achieve this, we exploited three widely practiced datasets based on public opinions about movies. We utilized merely one bidirectional long short-term memory (BiLSTM) layer along with a global pooling mechanism and achieved an accuracy of 80.500%, 85.780%, and 90.585% on MR, SST2 and IMDb datasets, respectively. We concluded that the performance metrics of our proposed approach are competitive with the recently published models, having comparatively complex architectures. Also, it is inferred that the proposed single-layered BiLSTM based architecture is computationally efficient and can be recommended for real-time applications in the field of sentiment analysis.
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Cited by
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Variational Quantum Classifier for Binary Classification: Real vs Synthetic Dataset
Maheshwari, D., Sierra-Sosa, D., & Garcia-Zapirain, B. (2022) · IEEE Access · doi:10.1109/access.2021.3139323 · Cited by 108
Self-citationPimpalkar, A., & Raj R, J. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117581 · Cited by 98
Metadata
- Authors
- Hameed, Z., & Garcia-Zapirain, B.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 73992-74001
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3016921356
- Cited by
- 269
- References
- 37
- FWCI
- 12.12
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- eVida research group, University of Deusto, Bilbao, Spain (IT 905-16)
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