The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model
Open accessAbstract
Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valencearousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide more fine-grained sentiment analysis. This study proposes a regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses an individual sentence as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependency across sentences can be considered in the prediction process. Experimental results show that the proposed method outperforms lexicon-based, regression-based, and NN-based methods proposed in previous studies.
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 1×
“The dimensional approach represents emotional states as continuous numerical values in multiple dimensions such as the valence-arousal (VA) space (Russell, 1980).”
BackgroundAn argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
Measuring emotion: The self-assessment manikin and the semantic differential
Bradley, M. M., & Lang, P. J. (1994) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/0005-7916(94)90063-9 · Cited by 9,538
Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., & Frasconi, P. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279181 · Cited by 8,542
Norms of valence, arousal, and dominance for 13,915 English lemmas
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0314-x · Cited by 1,959
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Calvo, R. A., & D'Mello, S. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.1 · Cited by 1,893 · cited 1×
“Sentiment analysis has been useful in the development of online applications for customer reviews and public opinion analysis (Pang and Lee 2008; Calvo and D'Mello 2010; Liu 2012; Feld-man 2013).”
MethodOpinion Mining with Deep Recurrent Neural Networks
Irsoy, O., & Cardie, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1080 · Cited by 384 · cited 1×
“…networks (NN) such as convolutional neural networks (CNN) (Kim, 2014; Kalchbrenner et al., 2014), recurrent neural networks (RNN) (Graves, 2012; Irsoy and Cardie, 2014) and long short-term memory (LSTM) (Wang et al., 2015; Liu et al., 2015) have been successfully employed for categorical…”
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Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis
Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.08.005 · Cited by 745
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2023) · Encyclopedia of Machine Learning and Data Science · doi:10.1007/978-1-4899-7502-7_907-2 · Cited by 518
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Chakraborty, I., Kim, M., & Sudhir, K. (2022) · Journal of Marketing Research · doi:10.1177/00222437211052500 · Cited by 84
Metadata
- Authors
- Wang, J., Yu, L.-C., Lai, K. R., & Zhang, X.
- Venue
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Published
- 2016
- Pages
- 225-230
- OpenAlex
- W2514588627
- Cited by
- 584
- References
- 9
- FWCI
- 26.32
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Wang, J., Yu, L.-C., Lai, K. R., & Zhang, X. (2016). Dimensional Sentiment Analysis Using a Regional CNN-LSTM Model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). https://doi.org/10.18653/v1/p16-2037
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