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Week 3 discussion 2 in MHA605: Business Intelligence (MHA2528A), Review Social Media Analytics and Reporting: Google Digital Marketing & E-commerce Certificate
Prior to beginning work on this discussion forum,
Read Chapter 6 and review Chapter 5 of your textbook, An Overview of Business Intelligence, Analytics, Data Science, and AI
Review webpages Text Analysis for Social MediaLinks to an external site. and What is Social Media Analytics?Links to an external site.
Review Social Media Analytics and Reporting: Google Digital Marketing & E-commerce CertificateLinks to an external site.
Obtaining relevant data about practical and applicable decision-making processes can be a daunting endeavor for healthcare organizations. Many types of textual content, such as articles, surveys, social media posts, and internet forums can be analyzed and lead to a range of possible outcomes. Researchers often use social media data to track social trends and attitudes.
The healthcare industry applies textual analytics to social media to understand complex issues, such as customer/patient care expectations, the use of third-party vendors, and patient experiences. According to Delen, Sharda, and Turban (2023), “This changing nature of data is forcing organizations to make text and Web analytics a critical part of their business intelligence/analytics infrastructure” (p.247). It is important to understand data mining, and its ability to aid organizations in positive decision-making processes, should benefit customers/patients. Data mining can lead to reduced patient risks, improved treatment effectiveness, and improved patient and community relationships.
For this discussion address the following in a minimum of 500 words:
Describe information extraction, topic tracking, summarization, categorization, clustering, concept linking, and question answering as they relate to text mining.
Explain why text mining is gaining popularity in the healthcare delivery system.
Define two popular applications of text mining in the healthcare delivery system, why are they popular and when are they applied.
Explain two popular application areas for sentimental analysis in the healthcare industry.
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