Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques

Saad Awadh Alanazi (), Ayesha Khaliq, Fahad Ahmad, Nasser Alshammari, Iftikhar Hussain, Muhammad Azam Zia, Madallah Alruwaili, Alanazi Rayan, Ahmed Alsayat and Salman Afsar
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Saad Awadh Alanazi: Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
Ayesha Khaliq: Department of Computer Science, National Textile University, Faisalabad 37300, Pakistan
Fahad Ahmad: Department of Basic Sciences, Deanship of Common First Year, Jouf University, Sakaka 72341, Saudi Arabia
Nasser Alshammari: Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
Iftikhar Hussain: Center for Sustainable Road Freight and Business Management, Heriot-Watt University, Edinburgh EH14 4AS, UK
Muhammad Azam Zia: Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 37300, Pakistan
Madallah Alruwaili: Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
Alanazi Rayan: Department of Computer Science, College of Science and Arts, Jouf University, Qurayyat 77413, Saudi Arabia
Ahmed Alsayat: Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
Salman Afsar: Department of Computer Science, University of Agriculture Faisalabad, Faisalabad 37300, Pakistan

IJERPH, 2022, vol. 19, issue 15, 1-27

Abstract: Public feelings and reactions associated with finance are gaining significant importance as they help individuals, public health, financial and non-financial institutions, and the government understand mental health, the impact of policies, and counter-response. Every individual sentiment linked with a financial text can be categorized, whether it is a headline or the detailed content published in a newspaper. The Guardian newspaper is considered one of the most famous and the biggest websites for digital media on the internet. Moreover, it can be one of the vital platforms for tracking the public’s mental health and feelings via sentimental analysis of news headlines and detailed content related to finance. One of the key purposes of this study is the public’s mental health tracking via the sentimental analysis of financial text news primarily published on digital media to identify the overall mental health of the public and the impact of national or international financial policies. A dataset was collected using The Guardian application programming interface and processed using the support vector machine, AdaBoost, and single layer convolutional neural network. Among all identified techniques, the single layer convolutional neural network with a classification accuracy of 0.939 is considered the best during the training and testing phases as it produced efficient performance and effective results compared to other techniques, such as support vector machine and AdaBoost with associated classification accuracies 0.677 and 0.761, respectively. The findings of this research would also benefit public health, as well as financial and non-financial institutions.

Keywords: mental health; financial text; machine learning; sentiment analysis; deep learning; the Guardian; support vector machine; AdaBoost; single layer convolutional neural network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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