KaydedilenlerDerginizi ekleyin

Dergiler / Research Journal of Business and Management / 2024 / Volume: 11 Issue: 2

UTILIZING TEXT MINING TO EXTRACT KNOWLEDGE AND CLASSIFY INFORMATION IN INDUSTRIAL ENGINEERING

Fereidoon Bidollahkhany(KARABUK UNIVERSITY)0009-0004-3064-9289
Mehdi Basiri(Tehran Azad University)0000-0002-2519-4679
Sayfa
—
Yayın Tarihi
2024-12-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The approach can be applied to other fields beyond industrial engineering, indicating its broader relevance and utility in the contemporary information age.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Purpose- In the modern information age, organizations face an overwhelming amount of textual data from various sources.
📊
Temel Bulgular & Veri:Methodology- The research follows a descriptive-survey approach, employing text-mining techniques to analyze a comprehensive dataset of scientific research articles from the Science Direct database between 2015 and 2020.
💡
Sonuç & Katkı:The approach can be applied to other fields beyond industrial engineering, indicating its broader relevance and utility in the contemporary information age.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:2015 Verileri
⚙️Değişkenler:Text mining · knowledge extraction · industrial engineering

Abstract

Purpose- In the modern information age, organizations face an overwhelming amount of textual data from various sources. The true potential of this data is realized when transformed into actionable knowledge. This study aims to create a text-based model for information classification and knowledge extraction in industrial engineering. Methodology- The research follows a descriptive-survey approach, employing text-mining techniques to analyze a comprehensive dataset of scientific research articles from the Science Direct database between 2015 and 2020. Data preprocessing was performed using Excel, while analysis was conducted with MATLAB software. The proposed model employs the nearest neighbor and support vector machine algorithms for robust text classification and knowledge extraction. Findings- The study demonstrates the model's effectiveness in systematically extracting valuable knowledge from diverse textual sources. It shows that this approach can facilitate information extraction without compromising data integrity, thereby contributing to knowledge management practices in industrial engineering. Conclusion- The text-based model developed in this study provides a reliable method for extracting knowledge from extensive textual datasets. The approach can be applied to other fields beyond industrial engineering, indicating its broader relevance and utility in the contemporary information age.

🔖

Bu Makaleye Atıf Yap

Bidollahkhany, F., Basiri, M. (2024). UTILIZING TEXT MINING TO EXTRACT KNOWLEDGE AND CLASSIFY INFORMATION IN INDUSTRIAL ENGINEERING. Research Journal of Business and Management, Volume: 11 Issue: 2. https://doi.org/10.17261/Pressacademia.2024.1948

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (18)

  1. Agha Kardan, A., & Kihaninejad, M. (2013). Providing a model for extracting information from text documents, based on text mining in the field of e-learning.
  2. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT 2019. https://arxiv.org/abs/1810.04805
  3. Geisler, E., & Wickramasinghe, N. (2009). Principles of Knowledge Management: Theory, Practice, and Cases. New York, NY: Routledge. ISBN 9781315686448
  4. Grootendorst, M. (2022). BERTopic: Neural Topic Modeling with Transformers and c-TF-IDF. Journal of Computational Social Science, 5(1), 345-360.
  5. Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of Emerging Technologies in Web Intelligence, 1(1), 60-76.
  6. Hotho, A., & Nürnberger, A. (2005). A brief survey of text mining. Journal for Language Technology and Computational Linguistics. https://doi.org/10.21248/jlcl.20.2005.68
  7. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.,. & Stoyanov, V. (2019). Roberta: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
  8. Müller, M., Hemati, W., & Reimers, N. (2021). Text Mining for Social Media Analysis: Trends and Tools. Information Systems Frontiers, 23(4), 1045-1060.
  9. Romero, C., Ventura, S., & Ventura, S. (2010). Educational Data Mining: A Review of the State-of-the-Art Systems. CRC Press. ISBN 9780429130120
  10. Sadeghi, S. A. M. M., & Moghan, M. (2014). Examining the place of data mining in the process of knowledge management and presenting a conceptual model for extracting knowledge. Presented a systematic process for the principled use of a large number of data sources.
  11. Soltaninejad, A., & Ahmadinejad, M. (2016). Text mining of computer engineering articles based on the documents retrieved from the Web of Science database using the famous K-means algorithm to create an algorithm for extracting Persian words.
  12. Sun, H., Yang, X., Zhang, Z., & Chen, Y. (2022). Advanced Sentiment Analysis with AI Models in Business Applications. Expert Systems with Applications, 200, 117164.
  13. Song, Q., & Colleagues. (2015). A centrality-based label propagation algorithm for community detection in networks.
  14. Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2022). Transformers in Text Mining: An Overview. Communications of the ACM, 65(7), 72-80.
  15. Wickramasinghe, N. (2008). Knowledge creation. In Knowledge Management Concepts, Methodologies, Tools and Application.
  16. Zeng, & Colleagues. (2016). Sentiment prediction by text mining using particle swarm optimization based on a feature selection search.
  17. Zong, C., Zhang, J., & Xia, R. (2021). Text Mining in Data Mining. Springer. ISBN 978-981-16-0100-21
  18. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.,. & Stoyanov, V. (2019). Roberta: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.