The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Abstract
Abstract The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.
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Cites
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Neural Architectures for Named Entity Recognition
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Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
Distributed representations, simple recurrent networks, and grammatical structure
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Opinion Mining with Deep Recurrent Neural Networks
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“Thus, it can be inferred that CRFs can take advantage of the entire sentence sequence to estimate probability for the sentence labelling making CRF a frequent final classification layer of bidirectional RNNs (T. Chen et al., 2017; Irsoy & Cardie, 2014; Lample et al., 2016; P. Liu et al., 2015).”
Background
Cited by
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
Sentiment Analysis Based on Deep Learning: A Comparative Study
Dang, N. C., Moreno-García, M. N., & De la Prieta, F. (2020) · Electronics · doi:10.3390/electronics9030483 · Cited by 593
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
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
AL-Smadi, M., Hammad, M. M., Al-Zboon, S. A., AL-Tawalbeh, S., & Cambria, E. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107540 · Cited by 62
Metadata
- Authors
- Do, H. H., Prasad, P., Maag, A., & Alsadoon, A.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 118
- Pages
- 272-299
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2895547478
- Cited by
- 614
- References
- 9
- FWCI
- 28.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Do, H. H., Prasad, P., Maag, A., & Alsadoon, A. (2019). Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2018.10.003
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