Published June 30, 2023 | Version CC BY-NC-ND 4.0
Journal article Open

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

Creators

  • 1. Research Scholar, Department of Computer & Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.
  • 2. Professor Department of Computer & Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.

Contributors

Contact person:

  • 1. Research Scholar, Department of Computer & Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.

Description

Abstract: The prediction of stock market prices based on the financial text sentiment classification using Machine Learning (ML) and Deep Learning (DL) models is becoming popular among researchers in the era of Big Data (BD). Nevertheless, owing to the lack of extensive analysis, most of the developed ML and DL models failed to achieve better classification results. Thus, for the real-time prediction of the polarity of the stock price, a Probability Tanh-Independently Recurrent Neural Network (PT-IndRNN)-based classification of the sentiment of the financial text data of Twitter is proposed to solve this problem. Primarily, by employing the corresponding API, the real-time financial data and Twitter data are extracted and stored in the MongoDB database using Apache Flume. This stored data with the historical big datasets are taken and pre-processed. Next, by deploying the proposed Hadoop Distributed File System (HDFS) clustering, the pre-processed stock market data and Twitter data in real-time, as well as the historical dataset, are combined separately. After that, the features are extracted from the clustered sentences. Then, by utilizing the SentiWordNet, the sentences chosen using Linear Scaling-Dwarf Mongoose Optimization Algorithm (LS-DMOA) are converted to negative and positive scores. In the end, the sentiment of the financial texts is classified by the PTh-IndRNN, which is proved by obtaining reliable result values.

Notes

Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved.

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Journal article: 2249-8958 (ISSN)

References

  • Nousi, C., & Tjortjis, C. (2021). A Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and Stock Twits Data. 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2021. 1-7
  • Gupta, R., & Chen, M. (2020). Sentiment Analysis for Stock Price Prediction. Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020, 213–218.
  • Mehtab, S., & Sen, J. (2020). Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models. 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, 447–453.
  • Kesavan, M., Karthiraman, J., Ebenezer Rajadurai, T., & Adhithyan, S. (2020). Stock Market Prediction with Historical Time Series Data and Sentimental Analysis of Social Media Data. Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020, 477–482.
  • Lin, Y.-L., Lai, C.-J., & Pai, P.-F. (2022). Using Deep Learning Techniques in Forecasting Stock Markets by Hybrid Data with Multilingual Sentiment Analysis. Electronics, 11(21), 1-19.
  • Bazzaz Abkenar, S., Haghi Kashani, M., Mahdipour, E., & Jameii, S. M. (2021). Big data analytics meets social media: A systematic review of techniques, open issues, and future directions. Telematics and Informatics, 57, 1-38.
  • Shao, C., & Chen, X. (2022). Deep-Learning-Based Financial Message Sentiment Classification in Business Management. Computational Intelligence and Neuroscience, 2022. 1-9.
  • Hassani, H., Beneki, C., Unger, S., Mazinani, M. T., & Yeganegi, M. R. (2020). Text mining in big data analytics. Big Data and Cognitive Computing, 4(1), 1–34.
  • Gupta, Y. K., & Sharma, N. (2020). Propositional aspect between apache spark and hadoop map-reduce for stock market data. Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 479–483.
  • Dong, S., & Liu, C. (2021). Sentiment Classification for Financial Texts Based on Deep Learning. Computational Intelligence and Neuroscience, 1-9.
  • Xiaofeng, W., Jinghua, Z., Chenxi, J., & Yiying, J. (2021). Research on sentiment classification of futures predictive texts based on BERT. Computing, 1-18.
  • Jaggi, M., Mandal, P., Narang, S., Naseem, U., & Khushi, M. (2021). Text mining of stocktwits data for predicting stock prices. Applied System Innovation, 4(1), 1–22.
  • Achyutha, P. N., Chaudhury, S., Bose, S. C., Kler, R., Surve, J., & Kaliyaperumal, K. (2022). User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis. Mathematical Problems in Engineering, 1-9.
  • Rodrigues, A. P., & Chiplunkar, N. N. (2022). A new big data approach for topic classification and sentiment analysis of Twitter data. Evolutionary Intelligence, 15(2), 877–887.
  • Demirbaga, U. (2021). HTwitt: a hadoop-based platform for analysis and visualization of streaming Twitter data. Neural Computing and Applications, 1-16.
  • Dong, J. (2020). Financial investor sentiment analysis based on FPGA and convolutional neural network. Microprocessors and Microsystems, 1-6.
  • Bourezk, H., Raji, A., Acha, N., & Barka, H. (2020). Analyzing Moroccan Stock Market using Machine Learning and Sentiment Analysis. 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020.1-5.
  • Dubey, A. K., Kumar, A., & Agrawal, R. (2021). An efficient ACO-PSO-based framework for data classification and preprocessing in big data. Evolutionary Intelligence, 14(2), 909–922.
  • Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., & Alfakeeh, A. S. (2022). Stock market prediction using machine learning classifiers and social media, news. Journal of Ambient Intelligence and Humanized Computing, 13(7), 3433–3456.
  • Deepika, N., & Nirupama Bhat, M. (2021). An Efficient Stock Market Prediction Method Based on Kalman Filter. Journal of The Institution of Engineers (India): Series B, 102(4), 629–644.
  • Aasi, B., Imtiaz, S. A., Qadeer, H. A., Singarajah, M., & Kashef, R. (2021). Stock price prediction using a multivariate multistep LSTM: A sentiment and public engagement analysis model. 2021 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2021 - Proceedings. 1-9.
  • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2021). A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. Journal of Big Data, 8(1), 1-28.
  • Yasir, M., Afzal, S., Latif, K., Chaudhary, G. M., Malik, N. Y., Shahzad, F., & Song, O. Y. (2020). An efficient deep learning based model to predict interest rate using twitter sentiment. Sustainability (Switzerland), 12(4). 1-16.
  • Zhao, L., Li, L., Zheng, X., & Zhang, J. (2021). A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts. Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021, 1233–1238
  • Xu, H., Chai, L., Luo, Z., & Li, S. (2020). Stock movement predictive network via incorporative attention mechanisms based on tweet and historical prices. Neurocomputing, 418, 326–339.

Subjects

ISSN: 2249-8958 (Online)
https://portal.issn.org/resource/ISSN/2249-8958#
Retrieval Number:100.1/ijeat.E41500612523
https://www.ijeat.org/portfolio-item/e41500612523/
Journal Website: www.ijeat.org
https://www.ijeat.org/
Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
https://www.blueeyesintelligence.org//