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Fusion: Practice and Applications
Volume 9 , Issue 1, PP: 59-69 , 2022 | Cite this article as | XML | Html |PDF


Multi-objective Decision Making Model for Stock Price Prediction Using Multi-source Heterogeneous Data Fusion

Authors Names :   Noura Metawa   1 *     Maha Mutawea   2  

1  Affiliation :  College of Business administration, University of Sharjah, UAE

    Email :  nmetawa@sharjah.ac.ae

2  Affiliation :  Delta University for Science and Technology, Gamasa, Egypt

    Email :  maha.mutawea@delta.gove.edu.eg

Doi   :   https://doi.org/10.54216/FPA.090105

Received: May 18, 2022 Accepted: August 16, 2022

Abstract :

Stock exchanges are developed as an essential component of economies, as they can promote financial and capital gain. The stock market is network of economic connections where share is bought and sold. Stock Market Prediction (SMP) is quite useful to investors. An effective forecast of stock prices is offer shareholders with suitable help in making appropriate decisions regarding if sell or purchase shares. The employ of Machine Learning (ML) and Sentiment Analysis (SA) on data in microblogging sites are developed as a famous approach to SMP. However, the heterogenous data fusion in stock market field is a big challenge. This paper introduces an effective Cat Swarm Optimization with Machine Learning Enabled Microblogging Sentiment Analysis for Stock Price Prediction technique. The presented model investigates the social media sentiments to foresee SPP. Firstly, the proposed model executes data pre-processing and Glove word embedding approach. Next, the weighted extreme learning machine approach was utilized for the classification of sentiments for SPP. Lastly, the CSO system was exploited for optimal adjustment of the parameters related to the WELM model. The experimental validation of the proposed approach was executed using microblogging data. The results show that the proposed method outperforms the previous studies.

Keywords :

Sentiment analysis; Microblogging; Stock price prediction; Heterogeneous Data Fusion; Machine learning; forecasting model

References :

[1] Chun, J., Ahn, J., Kim, Y. and Lee, S., 2021. Using deep learning to develop a stock price prediction

model based on individual investor emotions. Journal of Behavioral Finance, 22(4), pp.480-489.

[2] Hajiali, M., 2020. Big data and sentiment analysis: A comprehensive and systematic literature

review. Concurrency and Computation: Practice and Experience, 32(14), p.e5671.

[3] Gupta, I., Madan, T.K., Singh, S. and Singh, A.K., 2022. HiSA-SMFM: Historical and Sentiment

Analysis based Stock Market Forecasting Model. arXiv preprint arXiv:2203.08143.

[4] Daudert, T., 2021. Exploiting textual and relationship information for fine-grained financial sentiment

analysis. Knowledge-Based Systems, 230, p.107389.

[5] Issam, A., Mounir, A.K. and El Mendili Saida, E.M.F., 2022. Financial sentiment analysis of tweets

based on deep learning approach. Indonesian Journal of Electrical Engineering and Computer

Science, 25(3), pp.1759-1770.

[6] Hossain, M.S., Rahman, M.F., Uddin, M.K. and Hossain, M.K., 2022. Customer sentiment analysis and

prediction of halal restaurants using machine learning approaches. Journal of Islamic Marketing,


[7] Nawaz, U., Ali, A., Raza, U.A. and Shehzadi, K., 2021. A Survey: Sentimental Analysis on Product

Reviews Using (MLT) Machine Learning Techniques and Approaches. International Journal, 10(2).

[8] Kanakaraddi, S.G., Chikaraddi, A.K., Gull, K.C. and Hiremath, P.S., 2020, February. Comparison

study of sentiment analysis of tweets using various machine learning algorithms. In 2020 International

Conference on Inventive Computation Technologies (ICICT) (pp. 287-292). IEEE.

[9] Wadawadagi, R.S. and Pagi, V.B., 2020. Sentiment analysis on social media: Recent trends in machine

learning. Handbook of Research on Emerging Trends and Applications of Machine Learning, pp.508-


[10] Malawana, M.V.D.H.P. and Rathnayaka, R.T., 2020, December. The Public Sentiment analysis within

Big data Distributed system for Stock market prediction–A case study on Colombo Stock Exchange.

In 2020 5th International Conference on Information Technology Research (ICITR) (pp. 1-6). IEEE.

[11] Koukaras, P., Nousi, C. and Tjortjis, C., 2022, May. Stock Market Prediction Using Microblogging

Sentiment Analysis and Machine Learning. In Telecom (Vol. 3, No. 2, pp. 358-378). MDPI.

[12] Keramatfar, A., Amirkhani, H. and Bidgoly, A.J., 2022. Modeling Tweet Dependencies with Graph

Convolutional Networks for Sentiment Analysis. Cognitive Computation, pp.1-12

[13] Basiri, M.E., Nemati, S., Abdar, M., Asadi, S. and Acharrya, U.R., 2021. A novel fusion-based deep

learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 228, p.107242

[14] Yıldırım, S., Jothimani, D., Kavaklioğlu, C. and Başar, A., 2019, December. Deep learning approaches

for sentiment analysis on financial microblog dataset. In 2019 IEEE International Conference on Big

Data (Big Data) (pp. 5581-5584). IEEE

[15] Liu, J., Lu, Z. and Du, W., 2019, January. Combining enterprise knowledge graph and news sentiment

analysis for stock price prediction. In Proceedings of the 52nd Hawaii International Conference on

System Sciences

[16] Pasupulety, U., Anees, A.A., Anmol, S. and Mohan, B.R., 2019, June. Predicting stock prices using

ensemble learning and sentiment analysis. In 2019 IEEE Second International Conference on Artificial

Intelligence and Knowledge Engineering (AIKE) (pp. 215-222). IEEE

[17] Bouktif, S., Fiaz, A. and Awad, M., 2019, October. Stock market movement prediction using disparate

text features with machine learning. In 2019 Third International Conference on Intelligent Computing

in Data Sciences (ICDS) (pp. 1-6). IEEE.

[18] Oota, S.R., Manwani, N. and Bapi, R.S., 2018, December. fMRI semantic category decoding using

linguistic encoding of word embeddings. In International Conference on Neural Information

Processing (pp. 3-15). Springer, Cham.

[19] Wang, Y., Wang, A., Ai, Q. and Sun, H., 2019. Ensemble based fuzzy weighted extreme learning

machine for gene expression classification. Applied Intelligence, 49(3), pp.1161-1171.

[20] Huang, J., Asteris, P.G., Manafi Khajeh Pasha, S., Mohammed, A.S. and Hasanipanah, M., 2020. A

new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization

algorithm. Engineering with Computers, pp.1-12.

Cite this Article as :
Noura Metawa , Maha Mutawea, Multi-objective Decision Making Model for Stock Price Prediction Using Multi-source Heterogeneous Data Fusion, Fusion: Practice and Applications, Vol. 9 , No. 1 , (2022) : 59-69 (Doi   :  https://doi.org/10.54216/FPA.090105)