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Research Article
Smart Trading With NLP: A Journey Through Scopus Research on Stock Market Analysis
Prathamesh Birajdar1
Lavanya Vanga2
Sakshi Dama3
123 Department of CSE(AI&DS), Shree Siddheshwar College of Engineering, Solapur, Maharashtra, India.
Published Online: May-June 2024
Pages: 41-46
Cite this article
No DOIReferences
1. F. G. D. C. Ferreira, A. H. Gandomi and R. T. N. Cardoso, "Artificial Intelligence Applied to Stock Market Trading: A Review,"
in IEEE Access, vol. 9, pp. 30898-30917, 2021, doi: 10.1109/ACCESS.2021.3058133.
2. M. Izadi and M. N. Ahmadabadi, "On the Evaluation of NLP-based Models for Software Engineering," 2022 IEEE/ACM 1st
International Workshop on Natural Language-Based Software Engineering (NLBSE), Pittsburgh, PA, USA, 2022, pp. 48-50, doi:
10.1145/3528588.3528665.
3. P. Bose, S. Roy and P. Ghosh, "A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19
Research," in IEEE Access, vol. 9, pp. 78341-78355, 2021, doi: 10.1109/ACCESS.2021.3082108.
4. R. Sonbol, G. Rebdawi and N. Ghneim, "The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering
Tasks: A Systematic Mapping Review," in IEEE Access, vol. 10, pp. 62811-62830, 2022, doi: 10.1109/ACCESS.2022.3182372.
5. X. Wan, M. C. Lucic, H. Ghazzai and Y. Massoud, "Empowering Real-Time Traffic Reporting Systems With NLP-Processed Social
Media Data," in IEEE Open Journal of Intelligent Transportation Systems, vol. 1, pp. 159-175, 2020, doi:
10.1109/OJITS.2020.3024245.
6. R. Espinosa, L. Garriga, J. J. Zubcoff and J. -N. Mazón, "Linked Open Data mining for democratization of big data," 2014 IEEE
International Conference on Big Data (Big Data), Washington, DC, USA, 2014, pp. 17-19, doi: 10.1109/BigData.2014.7004479
7. P. C. Chhipa, R. Upadhyay, G. G. Pihlgren, R. Saini, S. Uchida and M. Liwicki, "Magnification Prior: A Self-Supervised Method for
Learning Representations on Breast Cancer Histopathological Images," 2023 IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV), Waikoloa, HI, USA, 2023, pp. 2716-2726, doi: 10.1109/WACV56688.2023.00274.
8. C. Wang et al., "An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled with Fully and SemiSupervised Reciprocal Learning," in IEEE Transactions on Medical Imaging, vol. 43, no. 1, pp. 392-404, Jan. 2024,
doi:10.1109/TMI.2023.3306781.
9. D. Goularas and S. Kamis, "Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data," 2019 International
Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Istanbul, Turkey, 2019, pp.12-17, doi:
10.1109/Deep-ML.2019.00011.
10. G. Karatas, O. Demir and O. Koray Sahingoz, "Deep Learning in Intrusion Detection Systems," 2018 International Congress on Big
Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkey, 2018, pp. 113-116,
doi:10.1109/IBIGDELFT.2018.8625278.
in IEEE Access, vol. 9, pp. 30898-30917, 2021, doi: 10.1109/ACCESS.2021.3058133.
2. M. Izadi and M. N. Ahmadabadi, "On the Evaluation of NLP-based Models for Software Engineering," 2022 IEEE/ACM 1st
International Workshop on Natural Language-Based Software Engineering (NLBSE), Pittsburgh, PA, USA, 2022, pp. 48-50, doi:
10.1145/3528588.3528665.
3. P. Bose, S. Roy and P. Ghosh, "A Comparative NLP-Based Study on the Current Trends and Future Directions in COVID-19
Research," in IEEE Access, vol. 9, pp. 78341-78355, 2021, doi: 10.1109/ACCESS.2021.3082108.
4. R. Sonbol, G. Rebdawi and N. Ghneim, "The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering
Tasks: A Systematic Mapping Review," in IEEE Access, vol. 10, pp. 62811-62830, 2022, doi: 10.1109/ACCESS.2022.3182372.
5. X. Wan, M. C. Lucic, H. Ghazzai and Y. Massoud, "Empowering Real-Time Traffic Reporting Systems With NLP-Processed Social
Media Data," in IEEE Open Journal of Intelligent Transportation Systems, vol. 1, pp. 159-175, 2020, doi:
10.1109/OJITS.2020.3024245.
6. R. Espinosa, L. Garriga, J. J. Zubcoff and J. -N. Mazón, "Linked Open Data mining for democratization of big data," 2014 IEEE
International Conference on Big Data (Big Data), Washington, DC, USA, 2014, pp. 17-19, doi: 10.1109/BigData.2014.7004479
7. P. C. Chhipa, R. Upadhyay, G. G. Pihlgren, R. Saini, S. Uchida and M. Liwicki, "Magnification Prior: A Self-Supervised Method for
Learning Representations on Breast Cancer Histopathological Images," 2023 IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV), Waikoloa, HI, USA, 2023, pp. 2716-2726, doi: 10.1109/WACV56688.2023.00274.
8. C. Wang et al., "An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled with Fully and SemiSupervised Reciprocal Learning," in IEEE Transactions on Medical Imaging, vol. 43, no. 1, pp. 392-404, Jan. 2024,
doi:10.1109/TMI.2023.3306781.
9. D. Goularas and S. Kamis, "Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data," 2019 International
Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Istanbul, Turkey, 2019, pp.12-17, doi:
10.1109/Deep-ML.2019.00011.
10. G. Karatas, O. Demir and O. Koray Sahingoz, "Deep Learning in Intrusion Detection Systems," 2018 International Congress on Big
Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), Ankara, Turkey, 2018, pp. 113-116,
doi:10.1109/IBIGDELFT.2018.8625278.
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