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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3APICQN9HK" target="_blank" >RIV/00216208:11320/22:PICQN9HK - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1660-4601/19/15/9695" target="_blank" >https://www.mdpi.com/1660-4601/19/15/9695</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ijerph19159695" target="_blank" >10.3390/ijerph19159695</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

—

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Environmental Research and Public Health

ISSN

1660-4601

e-ISSN

1660-4601

Volume of the periodical

19

Issue of the periodical within the volume

15

Country of publishing house

CH - SWITZERLAND

Number of pages

27

Pages from-to

1-27

UT code for WoS article

000839251900001

EID of the result in the Scopus database

2-s2.0-85136342127