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

Saad Awadh Alanazi¹, Ayesha Khaliq^2,3, Fahad Ahmad⁴

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia.

International Journal of Environmental Research and Public Health

|August 12, 2022

Summary

Sentiment analysis of financial news from The Guardian reveals public mental health trends. A single-layer convolutional neural network achieved 93.9% accuracy, outperforming other models in tracking financial sentiment.

Keywords:

AdaBoost deep learning financial text machine learning mental health sentiment analysis single layer convolutional neural network support vector machine the Guardian

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Area of Science:

Computational social science
Natural language processing
Public health informatics

Background:

Public financial sentiment impacts individuals, institutions, and policy.
Digital media, like The Guardian, offers insights into public mood.
Tracking financial sentiment is crucial for understanding societal well-being.

Purpose of the Study:

To track public mental health through sentiment analysis of financial news.
To evaluate the impact of financial policies on public sentiment.
To compare machine learning models for financial text sentiment classification.

Main Methods:

Data collected via The Guardian's API.
Financial news text processed using Support Vector Machine (SVM), AdaBoost, and a single-layer Convolutional Neural Network (CNN).
Model performance evaluated based on classification accuracy.

Main Results:

The single-layer CNN achieved the highest classification accuracy (0.939).
SVM and AdaBoost showed lower accuracies (0.677 and 0.761, respectively).
CNN demonstrated superior performance in sentiment classification of financial news.

Conclusions:

Sentiment analysis of financial news is a viable tool for public mental health monitoring.
CNNs are effective for analyzing financial text sentiment.
Findings benefit public health and financial institutions.