WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model-Reference-Cited by-同舟云学术

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WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model

Published:2021-07-15 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)
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Author:

Rasool Abdur,Jiang Qingshan,Qu Qiang,Ji Chaojie

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9517075/9517076/09517671.pdf?arnumber=9517671

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Attention-based Spatialized Word Embedding Bi-LSTM Model for Sentiment Analysis;Pertanika Journal of Science and Technology;2023-11-06

2. Unsupervised Deep Embedded Clustering for High-Dimensional Visual Features of Fashion Images;Applied Sciences;2023-02-22

3. Bi-Directional CNN-RNN Architecture with Group-Wise Enhancement and Attention Mechanisms for Cryptocurrency Sentiment Analysis;Applied Artificial Intelligence;2022-11-13

4. Perturb more, trap more: Understanding behaviors of graph neural networks;Neurocomputing;2022-07

5. ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis;PeerJ Computer Science;2022-03-17

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