skip to main content
research-article

Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

Authors Info & Claims
Published:09 May 2023Publication History
Skip Abstract Section

Abstract

For changing semantics, ontological and information presentation, as well as computational linguistics for Asian social networks, are one of the most essential platforms for offering enhanced and real-time data mapping, as well as huge data access across diverse big data sources on the web architecture, information extraction mining, statistical modeling and data modeling, database control, and so on. The concept of opinion or sentiment analysis is often used to predict or classify the textual data, sentiment, affect, subjectivity, and other emotional states in online text. Recognizing the message's positive and negative thoughts or opinions by examining the author's goals will aid in a better understanding of the text's content in terms of the stock market. An intelligent ontology and knowledge Asian social network solution can improve the effectiveness of a company's decision making support procedures by deriving important information about users from a wide variety of web sources. However, ontology is concerned primarily with problem-solving knowledge discovery. The utilization of Internet-based modernizations welcomed a significant effect on the Indian stock exchange. News related to the stock market in the most recent decade plays a vital role for the brokers or users. This article focuses on predicting stock market news sentiments based on their polarity and textual information using the concept of ontological knowledge-based Convolution Neural Network (CNN) as a machine learning approach. Optimal features are essential for the sentiment classification model to predict the stock's textual reviews' exact sentiment. Therefore, the swarm-based Artificial Bee Colony (ABC) algorithm is utilized with the Lexicon feature extraction approach using a novel fitness function. The main motivation for combining ABC and CNN is to accelerate model training, which is why the suggested approach is effective in predicting emotions from stock news.

REFERENCES

  1. [1] Fama E. F.. 1965. The behavior of stock-market prices. The Journal of Business 38, 1 (1965), 34--105.Google ScholarGoogle ScholarCross RefCross Ref
  2. [2] Cootner P.. 1964. The random character of stock market. J. Bus. (1964).Google ScholarGoogle Scholar
  3. [3] Fama E. F.. 1995. Random walks in stock market prices. Financial Analysts Journal 51, 1 (1995), 75--80.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Fama E., Fisher L., Jensen M., and Roll R.. 1969. The adjustment of stock prices to new information. Int. Econ. Rev. (1969).Google ScholarGoogle ScholarCross RefCross Ref
  5. [5] Bollen J., Mao H., and Zeng X.. 2011. Twitter mood predicts the stock market. J. Comput. Sci. (2011).Google ScholarGoogle ScholarCross RefCross Ref
  6. [6] Baker M. and Wurgler J.. 2006. Investor sentiment and the cross?section of stock returns. The journal of Finance 61, 4 (2006), 1645--1680.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Mehbodniya A., Alam I., Pande S., Neware R., Rane K. P., Shabaz M., and Madhavan M. V.. 2021. Financial fraud detection in healthcare using machine learning and deep learning techniques. In Security and Communication Networks, Vol. 2021, C. Chakraborty (Ed.). Hindawi Limited, 18. https://doi.org/10.1155/2021/9293877Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. [8] Mahajan K., Garg U., and Shabaz M.. 2021. CPIDM: A clustering-based profound iterating deep learning model for HSI segmentation. In Wireless Communications and Mobile Computing, Vol. 2021, V. Shanmuganathan (Ed.). Hindawi Limited, 112. https://doi.org/10.1155/2021/7279260Google ScholarGoogle Scholar
  9. [9] Kouloumpis E., Wilson T., and Moore J.. 2011. Twitter sentiment analysis: The good the bad and the OMG! In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media. 538541.Google ScholarGoogle Scholar
  10. [10] Tabari N., Seyeditabari A., Peddi T., Hadzikadic M., and Zadrozny W.. 2018. A comparison of neural network methods for accurate sentiment analysis of stock market tweets. In ecml pkdd 2018 Workshops, Springer, Cham, 51--65.Google ScholarGoogle Scholar
  11. [11] Jiang M., Lan M., and Wu Y.. 2017. ECNU at SemEval-2017 Task 5: An ensemble of regression algorithms with useful features for fine-grained sentiment analysis in the financial domain. In Proceedings of the 11th International Workshop on Semantic Evaluation. 885890. DOI: DOI: 10.18653/v1/S17-2152Google ScholarGoogle ScholarCross RefCross Ref
  12. [12] Sohangir S., Wang D., Pomeranets A., et al. 2018. Big data: Deep Learning for financial sentiment analysis. J. Big Data 5, 1 (2018). DOI: DOI: 10.1186/s40537-017-0111-6Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Sohangir S., Petty N., and Wang D.. 2018. Financial sentiment lexicon analysis. In Proceedings of the 12th IEEE International Conference on Semantic Computing. 286289.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Krishna V. B. and Pandey Kumar A.. 2018. Feature-based opinion mining and sentiment analysis using fuzzy logic. 7989.Google ScholarGoogle Scholar
  15. [15] Alaoui E. l., Gahi I., Mehsoosi R., Chaabi Y., Todoskoff A., and Kobi A.. 2018. A novel adaptable approach for sentiment analysis on big social data. J. Big Data 5 (2018), 12. DOI: DOI: 10.1186/s40537-018-1020-0Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Nuortimo K. and Härkönen J.. 2018. Opinion mining approach to study media-image of energy production. Implications for public acceptance and market deployment. Renew. Sust. Energy Rev. 96 (2018), 210217. DOI: DOI: 10.1016/j.rser.2018.07.018Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Gudelek U. M. and Boluk A. S.. 2017. A deep learning based stock trading Model with 2-D CNN trend detection. In IEEE Symposium Series on Computational Intelligence. 18.Google ScholarGoogle Scholar
  18. [18] Xing F. Z., Cambria E., and Welsch R. E.. 2018. Natural language based financial forecasting: A survey. Artif. Intell. Rev. 50, 1 (2018), 4973. https://doi.org/10.1007/s10462-017-9588-9Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Bühler K.. 1934. Sprachtheorie: Die Darstellungsfunktion der Sprache [Linguistics theory: Representation function of language]. Jena Fischer.Google ScholarGoogle Scholar
  20. [20] Boonpeng S. and Jetrakul P.. 2016. Decision support system for investing in stock market by using OAA-neural network. In 8th International Conference on Advanced Computational Intelligence (ICACI'16), IEEE, 1--6.Google ScholarGoogle Scholar
  21. [21] Carey C.. 2015. Predicting Stock Price Direction Through Data Mining and Machine Learning Techniques An Economics/Computer Science Interdepartmental Thesis.Google ScholarGoogle Scholar
  22. [22] Chomsky N.. 1956. Three models for the description of language. IEEE Trans. Inf. Theory 2, 3 (1956), 113124. https://doi.org/10.1109/TIT.1956.1056813Google ScholarGoogle ScholarCross RefCross Ref
  23. [23] Kirilenko P. A., Stepchenkova O. S., Kim H., and Li R. X.. 2018. Automated sentiment analysis in tourism: Comparison of approaches. J. Travel Res. 57, 8 (2018), 10121025.Google ScholarGoogle ScholarCross RefCross Ref
  24. [24] Lauren P., Qu G., Yang J., Watta P., and Amaury G.. 2018. Generating word embeddings from an extreme learning machine for sentiment analysis and sequence labeling tasks. Cogn. Comput. 10, 4 (2018), 625638.Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Abu-Salih B., Wongthongtham P., and Chan K. Y.. 2018. Twitter mining for ontology-based domain discovery incorporating machine learning. J. Knowl. Manage. (2018).Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Fang J., Guo L., and Niu Y.. 2010. Documents classification by using ontology reasoning and similarity measure. In Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery. 15351539.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Camous F., Blott S., and Smeaton A.. 2007. OntologyBased MEDLINE document classification. In Lecture Notes in Computer Science, Vol. 4414, Hochreiter S. and Wagner R. (Ed.). Bioinf. Res. Dev. (2007), 439452.Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Tsai P. W., Pan J. S., Liao B. Y., and Chu S. C.. 2009. Enhanced artificial bee colony optimization. Int. J. Innov. Comput. Inf Contr. 5, 12 (2009).Google ScholarGoogle Scholar
  29. [29] Nyberg K., Raiko T., Tinanen T., and Hyvonen E.. 2010. Document classification utilising ontologies and relations between documents. In Proceedings of the 8th Workshop on Mining and Learning with Graphs. 8693.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. [30] Liang S. D.. 2020. Optimization for deep convolutional neural networks: How slim can it go? IEEE Trans. Emerg. Top. Comput. Intell. 4 (2020), 171179.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Sun Y., Xue B., Zhang M., and Yen G. G.. 2020. Evolving deep convolutional neural networks for image classification. IEEE Trans. Evol. Comput. 24 (2020), 394407.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Ma B., Li X., Xia Y., and Zhang Y.. 2020. Autonomous deep learning: A genetic DCNN designer for image classification. Neurocomputing 379 (2020), 152161.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Baldominos A., Saez Y., and Isasi P.. 2018. Evolutionary convolutional neural networks: An application to handwriting recognition. Neurocomputing 283 (2018), 3852.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. [34] Chiong R., Fan Z., Adam M., and Neumann D.. 2018. A sentiment analysis-based machine learning approach for financial market prediction via news disclosures. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. DOI: DOI: 10.1145/3205651.3205682Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. [35] Sanober S., Alam I., Pande S., Arslan F., Rane K. P., Singh B. K., Khamparia A., and Shabaz M.. 2021. An enhanced secure deep learning algorithm for fraud detection in wireless communication. In Wireless Communications and Mobile Computing, Vol. 2021, V. Shanmuganathan (Ed.). Hindawi Limited, 114. https://doi.org/10.1155/2021/6079582Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. [36] Guo E., Jagota V., Makhatha M. E., and Kumar P.. 2021. Study on fault identification of mechanical dynamic nonlinear transmission system. Nonlin. Eng. 10, 1 (2021), 518525.Google ScholarGoogle ScholarCross RefCross Ref
  37. [37] Jianqiang Z. and Xiaolin G.. 2018. Deep convolution neural networks for twitter sentiment analysis. IEEE Access 6 (2018), 2325323260.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Chen S. and He H.. 2018. Stock prediction using convolutional neural network. IOP Conf. Ser.: Mater. Sci. Eng. 435, 1 (2018), 012026.Google ScholarGoogle Scholar
  39. [39] Karaboga D.. 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06. Erciyes University Press, Erciyes.Google ScholarGoogle Scholar
  40. [40] El Orche A. and Bahaj M.. 2019. Approach to use ontology based on electronic payment system and machine learning to prevent Fraud. In Proceedings of the 2nd International Conference on Networking, Information Systems & Security (NISS’19). ACM Press. https://doi.org/10.1145/3320326.3320369Google ScholarGoogle Scholar
  41. [41] Abu-Salih B., Wongthongtham P., and Yan Kit C.. 2018. Twitter mining for ontology-based domain discovery incorporating machine learning. J. Knowl. Manage. 22, 5 (2018), 949981. https://doi.org/10.1108/jkm-11-2016-0489Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Basturk B. and Karaboga D.. 2006. An artificial bee colony (ABC) algorithm for numeric function optimization. In Proceedings of the IEEE Swarm Intelligence Symposium.Google ScholarGoogle Scholar
  43. [43] Rao R. S., Narasimhama S. V. L., and Ramalingaraju M.. 2008. Int. J. Electr. Comput. Energ. Electr. Commun. Eng. 2, (2008), 116.Google ScholarGoogle Scholar
  44. [44] Karaboga D. and Akay B.. 2009. Int. J. Appl. Math. Comput. 214 (2009), 108.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Singh. 2009. An artificial bee colony algorithm for the leafconstrained minimum spanning tree problem. Applied Soft Computing 9, 2 (2009), 625--631.Google ScholarGoogle Scholar
  46. [46] Bolaji A. L., Khader A. T., A.Al-Betar M., and Awadallah M. A.. 2013. J. Theor. Appl. Inf. Technol. 47 (2013), 434.Google ScholarGoogle Scholar
  47. [47] Yan G. and Li C.. 2011. J. Comput. Inf. Syst. 7 (2011), 3309.Google ScholarGoogle Scholar
  48. [48] Batra R. and Daudpota S.. 2018. Integrating StockTwits with sentiment analysis for better prediction of stock price movement. In Proceedings of the International Conference on Computing, Mathematics, and Engineering Technologies.Google ScholarGoogle ScholarCross RefCross Ref
  49. [49] Dragoni M., Poria S., and Cambria E.. 2018. OntoSenticNet: A commonsense ontology for sentiment analysis. IEEE Intell. Syst. (2018), 7785.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Pimpalkar P., Karia J., Khan M., Anand S., and Mukherjee T.. 2017. Stock market prediction using machine learning. Int. J. Adv. Eng. Res. Dev. (2017), 68.Google ScholarGoogle Scholar
  51. [51] Tabari N., Seyeditabari A., Peddi T., Hadzikadic M., and Zadrozny W.. 2018. A comparison of neural network methods for accurate sentiment analysis of stock market tweets. In ecml pkdd 2018 Workshops, Springer, Cham, 51--65.Google ScholarGoogle Scholar
  52. [52] Chakraborty P., Pria U., Rony R., and Majumdar M.. 2017. Predicting stock movement using sentiment analysis of twitter feed. In Proceedings of the 6th International Conference on Informatics, Electronics, and Vision. DOI: DOI: 10.1109/ICIEV.2017.8338584Google ScholarGoogle ScholarCross RefCross Ref
  53. [53] Ahmed M., Sriram A., and Singh S.. 2014. Towards a generic framework for short term firm-specific stock forecasting. In Proceedings of the International Conference on Advances in Computing, Communications, and Informatics. 26812688.Google ScholarGoogle Scholar
  54. [54] Zhang L., Xiao K., Zhu H., Liu C., Yang J., and Jin B.. 2018. CADEN: A context-aware deep embedding network for financial opinions mining. In Proceedings of the IEEE International Conference on Data Mining. 757766.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Desai R.. 2018. Sentiment analysis of Twitter data. In Proceedings of the International Conference on Intelligent Computing and Control Systems. 114117.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Supervised Machine Learning Method for Ontology-based Financial Decisions in the Stock Market

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in

        Full Access

        • Published in

          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 5
          May 2023
          653 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3596451
          Issue’s Table of Contents

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 9 May 2023
          • Online AM: 5 November 2022
          • Accepted: 24 June 2022
          • Revised: 18 June 2022
          • Received: 16 February 2022
          Published in tallip Volume 22, Issue 5

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
        • Article Metrics

          • Downloads (Last 12 months)237
          • Downloads (Last 6 weeks)6

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!