###### Abstract  
The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients' experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the \"COVID-19 Tweets\" Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to \"pains\" and \"gains\" regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system. m 2024 14 8762  
care and aims to involve them as much as possible. As a result, the trend toward patient-centered care has been emphasized, as patients seek a more engaged experience with the use of health monitoring devices and a trusted relationship with their healthcare provider [3; 4; 5]. Patient experience is another significant quality index. There is a strong positive correlation between the outcome of a patient and their experience [6]. A patient's experience relies on what happened to the patient and how the patient perceived that experience. This also highlights the subtle difference between \"patient experience\" and \"patient satisfaction\", which are often used interchangeably. Moreover, the patient experience is related to their perception of care, and satisfaction accounts for their expectations of care. A vast amount of unstructured data regarding patients' healthcare experiences is present on social media, which is usually posted by patients or their family members [7; 8]. However, due to the ingrained complexity of processing and analyzing such data, this information is not systematically assessed and utilized to improve the healthcare system. Text analytics can play a more significant role in harnessing meaningful insights from social media; policymakers can obtain directions on improving and implementing better patient-centric care. This study incorporates natural language processing (NLP) to efficiently capture the maternal patient experience from a large-scale social media dataset containing Twitter tweets. We selected maternal patients as one of the cohorts to analyze the use of NLP to measure patient experience. Moreover, maternal health is critical to women's health and well-being, particularly in the United States (US), where maternal mortality rates are high. According to the Centers for Disease Control and Prevention (CDC), approximately 700 women die each year in the US due to pregnancy-related causes, and 60% of these deaths are preventable [9]. We analyze tweets to discover topics related to maternal health and their sentiments to understand the pains and gains expressed by patients, relatives, or friends. We used the IEEE coronavirus COVID-19 tweets dataset for our analysis [10].