摘要
由一名新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-冠状病毒的新研究-COVID-19是一篇报道的子主题。根据来自加拿大萨斯卡通的新闻报道，NewsRx记者的研究表明，"在大流行期间，痴呆症患者被确定为弱势人群。X（以前是Twitter）成为人们在COVID-19上寻求更新的重要信息来源，因此，识别与痴呆症相关的帖子（以前是Twitter）可以成为痴呆症患者及其照顾者的重要支持。"新闻记者从萨斯喀彻温大学的研究中得到一句话，挖掘和编码相关帖子可能会令人望而生畏，因为不相关帖子数量庞大，比例高。本研究的目标是使用自然语言处理和机器学习(ML)算法自动识别与痴呆症和COVID-19相关的帖子。我们使用自然语言处理和机器学习算法结合使用人工标记帖子来识别与痴呆症和COVID-19相关的帖子。我们使用了包含10万篇以上帖子的3个DATA集，评估了各种算法正确识别相关帖子的能力，结果表明(PR Etrained)转移学习算法在识别痴呆和COVID-19相关帖子方面优于传统的ML算法。Transformers(ALBERT)的传递学习算法Alite双向编码器表示的准确率为82.92%，曲线下的REA为83.53%，ALBERT的性能明显优于其他被测试的算法，进一步强调了Transformer学习算法在帖子分类中的优越性，ALBERT等传递学习算法对特定主题帖子的识别是非常有效的。当使用有限或相邻的数据进行训练时，强调了它们在其他ML算法中的优势，以及对其他涉及分析社交媒体帖子的研究的适用性。
Abstract
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."
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NSTL