## 2 Literature Review
### Research in Text Analytics
#### 2.2.2 Sentiment Analysis  
One of the most widely used tools to garner sentiment from text or voice messages is sentiment analysis, which classifies the underlying emotion as positive, negative, and neutral [89; 90; 91; 92]. We discuss three sentimental analysis methods: machine learning algorithms, rule-based systems, and lexicon-based approaches. A lexicon-based approach uses dictionaries of sentiment-laden words to classify text based on the presence or absence of particular words or phrases and utilize the term phrases, sentimental idioms, and expressions. On the other hand, a machine learning-based approach uses a computer model to train an existing dataset with defined emotions and uses those trained models to predict the sentiment of new text data [93]. The rule-based systems use a set of predefined rules to classify text based on specific patterns. The Valence Aware Dictionary and sEntiment Reasoner (VADER) is a sentiment analysis tool based on the rule-based system specifically designed to handle social media data, which often contains informal language (often used in social media texts), slang, and abbreviated words. It has gained significant attention recently due to its high accuracy and efficiency in analyzing sentiment in social media texts [94]. They showed that VADER's accuracy in identifying neutral sentiments outperformed other sentiment analysis tools, such as TextBlob and the Stanford CoreNLP. [PERSON] and [PERSON] (2019) [95] used VADER and NLTK sentiment analysis tools to interpret sentiments in Twitter data. Here, also, VADER achieved higher accuracy and was more capable of handling contextual information than NLTK. [PERSON] et al. (2020) [96] showed that VADER performed better than SentiStrength and AFINN in analyzing sentiments, irony, and sarcasm in online product reviews. Our study used the VADER sentiment analysis to analyze the maternal patient experience from Twitter. Different industries, such as tourism, politics, and marketing, extensively utilize the power of sentiment analysis [97] to discern and extract subjective insights from customer reviews and utilize those insights to improve their service [98]. Sentiment analysis is widely used in clinical research and health informatics to analyze patients' sentiments regarding their care and identify areas to improve healthcare quality. [PERSON] et al. (2016) [99] have reviewed several use cases for sentimental analysis in healthcare. Sentiment analysis gives decision-makers insights into how patients feel toward caregivers and treatment systems [100]. [PERSON] et al. (2014) [101] presented a mixed-method study to evaluate the patient experience and hospital quality from a small number of tweets. [PERSON] et al. (2016) [102] utilized a machine learning approach to analyze data from 2349 US hospitals over one year to determine the patient's experience, including their care, experience from hospital administration, and interaction with healthcare professionals. [PERSON] et al. (2016) [103] presented a study that analyzed emotions from the tweets of various cancer patients for unique cancer diagnostics. Similarly, [PERSON] et al. (2016) [104] have introduced a tool called SentiHealth-Cancer (SHC-pt) to identify the mental condition of cancer patients from social media. While surveys and patient feedback are commonly used to measure patient experience, patient journey mapping can capture it more comprehensively. [PERSON] (2021) [22] utilized sentimental analysis to provide valuable insights for improving empathetic and respectful care in clinical systems and enhancing patient-centered care. Polarity and subjectivity are the two most popular measures of sentiment analysis [105; 106; 107]. Subjective texts often have more complex and nuanced meanings than objective ones [108; 109]. For instance, a positive review of a restaurant may contain various subjective expressions such as \"the food was amazing\", \"the ambiance was fantastic\", or \"the service was outstanding\".