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Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition
Research Article | Published: 30 March 2025
IECE Transactions on Neural Computing Volume 1, Issue 1, 2025: 11-29
Volume 1, Issue 1, IECE Transactions on Neural Computing
Volume 1, Issue 1, 2025
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Muhammad Adnan Khan
Muhammad Adnan Khan
Gachon University, Republic of Korea
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IECE Transactions on Neural Computing, Volume 1, Issue 1, 2025: 11-29

Open Access | Research Article | 30 March 2025
Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition
by
Asif Ahamed Asif Ahamed 1 *
Md Tanvir Rahman Tarafder Md Tanvir Rahman Tarafder 1
SM Tamim Hossain Rimon SM Tamim Hossain Rimon 2
Nisher Ahmed Nisher Ahmed 1
1 College of Technology and Engineering, Westcliff University, Irvine, CA 92614, United States
2 Department of Management & Marketing, Qatar University, Doha, Qatar
* Corresponding Author: Asif Ahamed, [email protected]
DOI: 10.62762/TNC.2025.384898
Received: 24 February 2025, Accepted: 18 March 2025, Published: 30 March 2025  
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Abstract
Currently, a considerable amount of people are sending messages on social networks such as Twitter, Amazon and Facebook. These media is colossal with data and information. Bearing in mind the need for these social media platforms to extract the appropriate negative or positive emotions from users and even news articles, opinion mining is required. Opinion mining provides the ability to assess social media users' opinions as well as the provided knowledge that assists in emotion detection. Some issues that have been more prevalent, in social media, include the lack of sentiment accuracy, transparency, and accuracy in measuring the users' sentiments. In social media, a variety of solutions based on different methods have been suggested in an attempt to capture the red flag on user's sentiments. For that reason, in this paper a system designed for the comment sentiment recognition problem is proposed and named Fuzzy-BIEM. This is based on extended Markov model (EM) with Bi-LSTM neural network and fuzzy logic. Rules were constructed with the fuzzy approach, and the Bi-LSTM deep neural network performed the sentiment recognition. EMM was employed to enhance the performance of the deep neural network. In this case, the input data were customer data from Amazon, Twitter, Facebook, Covid-19 fake news, and the Amazon fake news network. The application of fuzzy logic to the Fuzzy-BIEM approach did result in an increase of average emotion recognition accuracy. When fuzzy logic was used, the accuracy attained was 96.75%. Compared to the Fuzzy-BIEM approach without fuzzy logic, this is an increase of 7.62%. This was also an increase of 5.02% to the CSO-LSTMNN method.
Graphical Abstract
Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition
Keywords
opinion mining
sentiment analysis
extended markov model
deep neural network
fuzzy logic
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest. 
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Ahamed, A., Tarafder, M. T. R., Rimon, S. T. H., & Ahmed, N. (2025). Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition. IECE Transactions on Neural Computing, 1(1), 11–29. https://doi.org/10.62762/TNC.2025.384898
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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