首页|University of Saskatchewan Reports Findings in COVID-19 [Iden tifying X (Formerly Twitter) Posts Relevant to Dementia and COVID- 19: Machine Le arning Approach]
University of Saskatchewan Reports Findings in COVID-19 [Iden tifying X (Formerly Twitter) Posts Relevant to Dementia and COVID- 19: Machine Le arning Approach]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Coronavirus - COVID-19 is the sub ject of a report. According to news reporting originating in Saskatoon, Canada, by NewsRx journalists, research stated, "During the pandemic, patients with deme ntia were identified as a vulnerable population. X (formerly Twitter) became an important source of information for people seeking updates on COVID-19, and, the refore, identifying posts (formerly tweets) relevant to dementia can be an impor tant support for patients with dementia and their caregivers." The news reporters obtained a quote from the research from the University of Sas katchewan, "However, mining and coding relevant posts can be daunting due to the sheer volume and high percentage of irrelevant posts. The objective of this stu dy was to automate the identification of posts relevant to dementia and COVID-19 using natural language processing and machine learning (ML) algorithms. We used a combination of natural language processing and ML algorithms with manually an notated posts to identify posts relevant to dementia and COVID-19. We used 3 dat a sets containing more than 100,000 posts and assessed the capability of various algorithms in correctly identifying relevant posts. Our results showed that (pr etrained) transfer learning algorithms outperformed traditional ML algorithms in identifying posts relevant to dementia and COVID-19. Among the algorithms teste d, the transfer learning algorithm A Lite Bidirectional Encoder Representations from Transformers (ALBERT) achieved an accuracy of 82.92% and an a rea under the curve of 83.53%. ALBERT substantially outperformed th e other algorithms tested, further emphasizing the superior performance of trans fer learning algorithms in the classification of posts. Transfer learning algori thms such as ALBERT are highly effective in identifying topic-specific posts, ev en when trained with limited or adjacent data, highlighting their superiority ov er other ML algorithms and applicability to other studies involving analysis of social media posts."
SaskatoonCanadaNorth and Central Ame ricaAlgorithmsBrain Diseases and ConditionsCOVID-19Central Nervous Syste m Diseases and ConditionsCoronavirusCyborgsDementiaEmerging TechnologiesHealth and MedicineMachine LearningMental HealthNatural Language Process ingNeurodegenerative Diseases and ConditionsRNA VirusesSARS-CoV-2Severe Acute Respiratory Syndrome Coronavirus 2Social MediaViralVirology
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