Context-sensitive lexicon for imbalanced text sentiment classification using bidirectional LSTM
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-021-01866-0
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yiping Gao & Liang Gao & Xinyu Li & Yuwei Zheng, 2020. "A zero-shot learning method for fault diagnosis under unknown working loads," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 899-909, April.
- Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
- Longhua Xu & Chuanzhen Huang & Chengwu Li & Jun Wang & Hanlian Liu & Xiaodan Wang, 2021. "Estimation of tool wear and optimization of cutting parameters based on novel ANFIS-PSO method toward intelligent machining," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 77-90, January.
- Qiwu Zhu & Qingyu Xiong & Zhengyi Yang & Yang Yu, 2023. "A novel feature-fusion-based end-to-end approach for remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3495-3505, December.
- Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.
- Jianliang He & Yuxin Sun & Chen Yin & Yan He & Yulin Wang, 2023. "Cross-domain adaptation network based on attention mechanism for tool wear prediction," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3365-3387, December.
- Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
- Carlos A. Escobar & Megan E. McGovern & Ruben Morales-Menendez, 2021. "Quality 4.0: a review of big data challenges in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2319-2334, December.
- Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
- Xianli Liu & Bowen Zhang & Xuebing Li & Shaoyang Liu & Caixu Yue & Steven Y. Liang, 2023. "An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 885-902, February.
- Chuanhui Zuo & Jialong Wang & Mingping Liu & Suhui Deng & Qingnian Wang, 2023. "An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN," Energies, MDPI, vol. 16(14), pages 1-17, July.
- Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2024. "State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 147-160, January.
- Jianwei Wu & Jiaqi Wang & Huanguo Chen, 2024. "A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm," Mathematics, MDPI, vol. 12(15), pages 1-22, August.
More about this item
Keywords
Sentiment classification; Recurrent neural networks; Word embedding; Long Short Term Memory; Imbalanced data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-021-01866-0. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.