# TR-GPT-CF: A Topic Refinement Method Using GPT and Coherence Filtering
## 1 Introduction and State of the Art  
Topic modeling is an area of natural language processing (NLP) that employs statistical techniques to identify hidden topics or themes in documents [1]. It is widely utilized in various disciplines to aid in the extraction of patterns from large quantities of texts and documents [2]. As a result of its ability to assist the processing of enormous amounts of text data, topic modeling is a beneficial tool in a variety of sectors, including social media analysis, market research, and healthcare [3]. This versatility across domains is one of the most notable advantages of topic modeling [4]. A vast amount of user-generated content is available on the internet and social media, which can be difficult to examine [5]. Topic modeling enables businesses to better understand consumer preferences, opinions, and perceptions of their product by extracting topics of interest for a brand or company [6]. Topic modeling also helps substantially in market research and advertising campaigns since it enables businesses to discover consumer behavioral patterns [7], identify new trends, and customize consumer messaging to certain target markets [8]. In healthcare, topic modeling has been employed to facilitate text classification, where clinical notes are analyzed as collections of topics to enhance the categorization process [9]. Building on existing research findings [10], topic modeling approaches were used to examine and categorize the various public views and feelings on statins. This study identified and organized common themes and topics that arose during statin debates. Key topics identified included side effects, dietary interactions, hesitancy regarding statin use, and the perception of bias in the pharmaceutical sector. The extensive analysis offered by [10] helps illuminate the elements that influence public acceptance and resistance to statins, delivering crucial insights into community health dynamics. For these applications of topic modeling, the coherence and interpretability of topics are crucial [11]. In the text classification of electronic health records, topic models enable the selection of topics as features for predictive tasks, which increases the interpretability of these classification models [12]. This enhanced interpretability is critical for the clinical domain, particularly for decision making [9]. Effective applications in information retrieval rely on a comprehensive understanding of the result derived from topic modeling [13; 14]. The primary topic modeling technique utilized in these applications is Latent Dirichlet Allocation (LDA) [15; 16]. LDA is a strong statistical model that identifies latent themes within a corpus by assuming that documents are mixtures of topics and topics are mixtures of words [17; 18]. This method provides a brief overview of the document collection as well as guided exploration of the identified topics. A topic is intuitively characterized by a collection of words. Different topics may share words, and a document might be associated with multiple topics. A user can explore the themes to gain an overview of the corpus without reading all the documents and can focus on a specific topic by exploring only the texts that are closely related to it. For instance, a topic with the words \"computer\", \"model\", \"algorithm\", \"data\", and \"mathematical\" may be correlated with documents regarding computation [19; 20]. In addition to LDA, numerous additional topic modeling methodologies are commonly employed, each possessing distinct characteristics [21]. Non-negative Matrix Factorization (NMF) provides a linear algebraic method that generally yields more interpretable subjects by restricting topic weights to non-negative values [22]. The Correlated Topic Model (CTM) [23; 24] enhances LDA by permitting correlations among topics, thus providing a more accurate representation of real-world data characterized by overlapping themes.