The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN
Open accessAbstract
Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.
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Neural Architectures for Named Entity Recognition
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Opinion Mining with Deep Recurrent Neural Networks
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Cited by
Deep Learning--based Text Classification: A Comprehensive Review
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2022) · ACM Computing Surveys · doi:10.1145/3439726 · Cited by 1,538
Text Classification Algorithms: A Survey
Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019) · Information · doi:10.3390/info10040150 · Cited by 1,312
A review on sentiment analysis and emotion detection from text
Nandwani, P., & Verma, R. (2021) · Social Network Analysis and Mining · doi:10.1007/s13278-021-00776-6 · Cited by 869
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
Ma, C., Dai, G., & Zhou, J. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3055258 · Cited by 429
Metadata
- Authors
- Chen, T., Xu, R., He, Y., & Wang, X.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 72
- Pages
- 221-230
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2556605533
- Cited by
- 730
- References
- 6
- FWCI
- 42.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Chen, T., Xu, R., He, Y., & Wang, X. (2017). Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2016.10.065
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