Machine Learning-Based Practical Social-Sensor Provision for Psychological Well-Being Intensive Care Consuming Twitter Data
| International Journal of Computer Science and Engineering |
| © 2023 by SSRG - IJCSE Journal |
| Volume 10 Issue 3 |
| Year of Publication : 2023 |
| Authors : S. Amutha |
10.14445/23488387/IJCSE-V10I3P101
How to Cite?
S. Amutha, "Machine Learning-Based Practical Social-Sensor Provision for Psychological Well-Being Intensive Care Consuming Twitter Data," SSRG International Journal of Computer Science and Engineering , vol. 10, no. 3, pp. 1-10, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I3P101
Abstract:
Respectively, using social media platforms remains viewed as a cloud of public sensors inside the social sensor ecosystem that makes up social media platforms. To overcome the restrictions of conventional health administration systems used for large-scale mental health surveillance. Although the current methods in the literature offer online mental disease screening, they are challenging to use in early detection. This study develops a general framework to facilitate proactive mental health monitoring, focusing on the Twitter platform. To achieve reliable results in sentiment analysis, comprehensive data spring scrubbing and preprocessing of the tweets are provided by consuming unvarying terms founded on experiential patterns. To circumvent the shortcomings of conventional classifiers, a machine learning apparatus, particularly LSTM, remains utilized for the initial discovery of at-risk public devices grounded on unique occasion explanations. Experimental results show that the suggested mechanism works better for accurate prediction than the current methods.
Keywords:
Machine learning, Sentiment analysis, Large-scale mental health surveillance, Data, Social media
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