## 2 Literature Review
### Research in Text Analytics
#### 2.2.1 Topic Modeling  
Topic modeling is an effective and practical tool in NLP for analyzing large text documents [62]. Topic modeling automatically groups words into topics and identifies relationships between documents within a dataset. In other words, it aims to characterize a text (such as articles, social media postings, survey results, and interview responses) as a distribution over topics and the topics as a distribution over words. The number of topics and the words within each topic are measured using a coherence score, and the similarity of these words to each other is measured [63]. In some of the studies, perplexity is used to find the optimal number of topics [64; 65]. The lower the value of perplexity, the more accurate it gets. In [PERSON] et al. (2021), the authors compared the perplexity-based model and the coherence-based model and concluded that although perplexity-based models produce simpler LDA models, the topic quality was poor. Latent Dirichlet allocation (LDA) is one of the most widely used topic modeling algorithms [66; 67; 68; 69; 70] to represent a group of documents based on their underlying themes. Itis an unsupervised learning method; i.e., the topics are identified without prior knowledge of their content [71]. LDA utilizes the score to measure the optimal number of topics that provide the maximum coherence value [72]. LDA aims to determine the range of issues in a specific document. LDA has been applied to a wide range of NLP tasks, which include topic classification from journals and newsgroups [69; 73]. LDA has also been widely used in the medical and healthcare sectors to analyze and classify various types of healthcare data. Applications of LDA in healthcare include analyzing electronic health records (EHRs) and patient feedback responses to prioritize patient experience improvement initiatives. LDA can analyze EHRs that contain a wealth of information, including clinical notes, diagnosis codes, and laboratory results, to identify patterns in patient data, such as comorbidities, medication use, side effects, disease progression, and treatment outcomes [74; 75; 76; 77]. Several researchers have used LDA to analyze patient feedback from hospital surveys and online reviews to identify factors influencing patient satisfaction. They found that topics such as communication, nursing care, staff attitude, care quality, waiting time, facility quality, and overall experience impact patient reviews of the hospital [78; 79; 80]. Improvements to various systems, such as appointment scheduling and billing, are crucial for improving the patient experience. The use of social media analysis in evaluating patient experience utilizes LDA techniques. [PERSON] et al. (2020) [71] used LDA to analyze over 176,000 Reddit threads that provided feedback about dermatology patients' experiences. [PERSON] (2021) [22] utilized patient feedback from social media (Twitter and Reddit) and applied LDA to evaluate breast cancer patients' experiences, find the latent topics shared by the patients, and evaluate the sentiments behind those topics. In addition to identifying topics related to patient experience, LDA can also be used to analyze changes in patient experience over time. [PERSON] et al. (2020) [81] used LDA to analyze patient feedback over three years to identify changes in patient experience. They found that while topics related to communication and staff attitude remained consistent, topics related to waiting time and access to care became more prominent in later years. Alongside LDA, there are other notable methods such as NMF (non-negative matrix factorization) [82], Top2 Vec [83], and BERTopic [84], which are also used in different studies to model topics [85]. NMF is a kind of linear algebraic algorithm, which is decompositional, non-probabilistic, and uses matrix factorization [86]. Top2 Vec is a fairly new algorithm which utilizes word embeddings and vectorization of text data to recognize semantics [83; 85].