CLASSIFICATION OF A DISRUPTOR AREAS OF INFLUENCE USING NEURAL NETWORKS
DOI:
https://doi.org/10.30890/2567-5273.2024-33-00-045Keywords:
communications, convolutional neural network, project-oriented organization, disruptor, Word2Vec, machine learning.Abstract
In today's information-intensive world, effective communication is a key element of success in object-oriented organizations, especially those that use virtual teams, so identifying and solving communication problems is essential.Analysis of research inMetrics
References
L. P. Hung and S. Alias, “Beyond sentiment analysis: a review of recent trends in text based sentiment analysis and emotion detection,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 27, no. 1, pp. 84–95, Jan. 2023, doi: 10.20965/jaciii.2023.p0084.
D. E. Cahyani, A. P. Wibawa, D. D. Prasetya, L. Gumilar, F. Akhbar, and E. R. Triyulinar, “Text-based emotion detection using CNN-BiLSTM,” in 2022 4th International Conference on Cybernetics and Intelligent System (ICORIS), Oct. 2022, pp. 1–5. doi: 10.1109/ICORIS56080.2022.10031370.
A. R. Abas, I. Elhenawy, M. Zidan, and M. Othman, “BERT-CNN: A deep learning model for detecting emotions from text,” Computers, Materials & Continua, vol. 71, no. 2, pp. 2943–2961, 2022, doi: 10.32604/cmc.2022.021671.
Kontsevyi, Vladyslav & Voitenko, Oleksandr. (2023). Communications disruptor in project-oriented organisations. 1-4. 10.1109/CSIT61576.2023.10324097.
F. Ullah, X. Chen, S. B. H. Shah, S. Mahfoudh, M. A. Hassan, and N. Saeed, “A novel approach for emotion detection and sentiment analysis for low resource urdu language based on CNN-LSTM,” Electronics, vol. 11, no. 24, p. 4096, Dec. 2022, doi: 10.3390/electronics11244096.
M. A. Riza and N. Charibaldi, “Emotion detection in Twitter social media using long short-term memory (LSTM) and fast text,” International Journal of Artificial Intelligence & Robotics (IJAIR), vol. 3, no. 1, pp. 15–26, May 2021, doi: 10.25139/ijair.v3i1.3827.
Suissa, Omri & Elmalech, Avshalom & Zhitomirsky-Geffet, Maayan. (2023). Text Analysis Using Deep Neural Networks in Digital Humanities and Information Science, doi: 10.1002/asi.24544.
M. Hasan, E. Rundensteiner, and E. Agu, “Automatic emotion detection in text streams by analyzing Twitter data,” International Journal of Data Science and Analytics, vol. 7, no. 1, pp. 35–51, Feb. 2019, doi: 10.1007/s41060-018-0096-z.
J. Herzig, M. Shmueli-Scheuer, and D. Konopnicki, “Emotion detection from text via ensemble classification using word embeddings,” in Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, Oct. 2017, pp. 269–272. doi: 10.1145/3121050.3121093.
B. T. Atmaja and M. Akagi, “Deep learning-based categorical and dimensional emotion recognition for written and spoken text,” IPTEK Journal of Proceedings Series, 2019.
Z. Jianqiang and G. Xiaolin, “Comparison research on text pre-processing methods on Twitter sentiment analysis,” IEEE Access, vol. 5, pp. 2870–2879, 2017, doi: 10.1109/ACCESS.2017.2672677.
F. Alrasheedi, X. Zhong, and P. C. Huang, “Padding module: learning the padding in deep neural networks,” IEEE Access, vol. 11, pp. 7348–7357, 2023, doi: 10.1109/ACCESS.2023.3238315.
A. K. Gautam and A. Bansal, “Effect of features extraction techniques on cyberstalking detection using machine learning framework,” Journal of Advances in Information Technology, vol. 13, no. 5, pp. 486–502, 2022, doi: 10.12720/jait.13.5.486-502. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
T. Adewumi, F. Liwicki, and M. Liwicki, “Word2Vec: optimal hyperparameters and their impact on natural language processing downstream tasks,” Open Computer Science, vol. 12, no. 1, pp. 134–141, Mar. 2022, doi: 10.1515/comp-2022-0236
Visa, Sofia & Ramsay, Brian & Ralescu, Anca & Knaap, Esther. (2011). Confusion Matrix-based Feature Selection.. CEUR Workshop Proceedings. 710. 120-127.
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