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Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition
Research Article  ·  Published: 30 March 2025
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ICCK Transactions on Neural Computing
Volume 1, Issue 1, 2025: 11-29
Research Article Open Access

Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition

by
Asif Ahamed Asif Ahamed 1 * ORCID
Md Tanvir Rahman Tarafder Md Tanvir Rahman Tarafder 1 ORCID
SM Tamim Hossain Rimon SM Tamim Hossain Rimon 2 ORCID
Nisher Ahmed Nisher Ahmed 1 ORCID
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]
Volume 1, Issue 1
Volume 1, Issue 1 2025
Received: 24 February 2025 Accepted: 18 March 2025 Published: 30 March 2025 CrossMark
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DOI: 10.62762/TNC.2025.384898
ARK: ark:/57805/tnc.2025.384898
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Article Information

Published in ICCK Transactions on Neural Computing
Volume/Issue Volume 1, Issue 1, 2025
Pages 11-29

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. ICCK Transactions on Neural Computing, 1(1), 11–29. https://doi.org/10.62762/TNC.2025.384898
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TY  - JOUR
AU  - Ahamed, Asif
AU  - Tarafder, Md Tanvir Rahman
AU  - Rimon, SM Tamim Hossain
AU  - Ahmed, Nisher
PY  - 2025
DA  - 2025/03/30
TI  - Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition
JO  - ICCK Transactions on Neural Computing
T2  - ICCK Transactions on Neural Computing
JF  - ICCK Transactions on Neural Computing
VL  - 1
IS  - 1
SP  - 11
EP  - 29
DO  - 10.62762/TNC.2025.384898
UR  - https://www.icck.org/article/abs/TNC.2025.384898
KW  - opinion mining
KW  - sentiment analysis
KW  - extended markov model
KW  - deep neural network
KW  - fuzzy logic
AB  - 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.
SN  - 3068-7519
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
BibTeX Format
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@article{Ahamed2025Bidirectio,
  author = {Asif Ahamed and Md Tanvir Rahman Tarafder and SM Tamim Hossain Rimon and Nisher Ahmed},
  title = {Bidirectional Deep Learning and Extended Fuzzy Markov Model for Sentiments Recognition},
  journal = {ICCK Transactions on Neural Computing},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {11-29},
  doi = {10.62762/TNC.2025.384898},
  url = {https://www.icck.org/article/abs/TNC.2025.384898},
  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.},
  keywords = {opinion mining, sentiment analysis, extended markov model, deep neural network, fuzzy logic},
  issn = {3068-7519},
  publisher = {Institute of Central Computation and Knowledge}
}

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