Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks-Reference-Cited by-同舟云学术

Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks

Published:2020-07-05 Issue:1 Volume:10 Page:
ISSN:1869-5450
Container-title:Social Network Analysis and Mining
language:en
Short-container-title:Soc. Netw. Anal. Min.

Author:

Ombabi Abubakr H.^ORCID,Ouarda Wael,Alimi Adel M.

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s13278-020-00668-1.pdf

Reference64 articles.

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2. Al-ayyoub M, Nuseir A (2016) Hierarchical classifiers for multi-way sentiment analysis of Arabic reviews. Int J Adv Comput Sci Appl 7(2):531–539

3. Al-kabi MN, Gigieh AH, Alsmadi IM, Wahsheh HA (2014) Opinion mining and analysis for Arabic language. Int J Adv Comput Sci Appl 5(5):181–195

4. Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y (2018) Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-018-0799-4

5. Alayba AM, Palade V, England M, Iqbal R (2018) Improving sentiment analysis in Arabic using word representation. In: 2nd international workshop on Arabic and derived script analysis and recognition (ASAR), pp 13–18

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