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• W4290520210 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." @default.
• W4290520210 created "2022-08-08" @default.
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• W4290520210 date "2022-08-06" @default.
• W4290520210 modified "2023-10-06" @default.
• W4290520210 title "Public’s Mental Health Monitoring via Sentimental Analysis of Financial Text Using Machine Learning Techniques" @default.
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• W4290520210 doi "https://doi.org/10.3390/ijerph19159695" @default.
• W4290520210 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35955051" @default.
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