Version 1
: Received: 2 June 2023 / Approved: 5 June 2023 / Online: 5 June 2023 (13:18:24 CEST)
How to cite:
Chou, H.; Cho, T. Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI. Preprints2023, 2023060323. https://doi.org/10.20944/preprints202306.0323.v1
Chou, H.; Cho, T. Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI. Preprints 2023, 2023060323. https://doi.org/10.20944/preprints202306.0323.v1
Chou, H.; Cho, T. Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI. Preprints2023, 2023060323. https://doi.org/10.20944/preprints202306.0323.v1
APA Style
Chou, H., & Cho, T. (2023). Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI. Preprints. https://doi.org/10.20944/preprints202306.0323.v1
Chicago/Turabian Style
Chou, H. and Tsai-Lun Cho. 2023 "Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI" Preprints. https://doi.org/10.20944/preprints202306.0323.v1
Abstract
For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.
Keywords
text mining; semantic analysis; labeling bull-bear words; futures corpus; generative AI
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
