## 2 Related Work  
Topic models (TM) are considered more appropriate to extract topics because of their ability to extract implicit and explicit coherent topics. TM can be dated back to 1990 when [PERSON] et al. [22] proposed Latent Semantic Indexing (LSI). LSI uses a singular value decomposition (SVD) of a large term-document matrix to identify a linear subspace such that the relationship between the term and document are captured. LSI was then adopted by various studies, such as [PERSON] et al. [23]. However, LSI is limited as the technique does not assign a probability to the topic. In respect to this, [PERSON] [24] developed probabilistic latent semantic indexing (pLSI). pLSI was also criticised for not being able to account for generative probabilistic models of the documents. Thus, Latent Dirichlet Allocation (LDA) was developed by [PERSON] et al. [2] as an improvement. LDA is a generative probabilistic model that captures the important intra-word/document statistical structure via a mixing distribution. LDA assumes that each document is associated with a topic distribution. Thus, topic assignment strongly relies on local co-occurrence. LDA has been used extensively for topic modelling [25; 26; 27; 28; 29]. For example, [PERSON] and [PERSON] [30] applied LDA to a dataset containing 10,594 airline-customers complaints from two Turkish airlines during the COVID-19 pandemic. Their study generated seven topics as the latent topics that customers complaints were about and used a qualitative human interpretation approach to validate the topics. Several studies [31; 32; 33; 34] employed LDA to extract topics from a financial policy statements document. [PERSON] et al. [35] utilised LDA to discover topics from 219 business intelligence (BI) articles within the banking domain and found that the most prominent topic in the BI banking literature is credit. [PERSON] et al. [36] used LDA to generate six topics from 2702 comments of signers of the e-petition against the Bank of America. [PERSON] and [PERSON] [37] utilised LDA to extract topics from eight Morocco mobile banking app reviews and showed security, services, quality, and interface as the topics customers talked about. [PERSON] [38] applied LDA to generate topics from 26 monetary policy statements of Lesotho's central bank. [PERSON] et al. [39] utilised LDA to extract 40 topics from a dataset containing 86,803 consumer financial protection Bureau (CFPB) consumer complaints. This is beneficial to CFPB to understand and monitor what aspect of financial service the complaint narrative was about. However, the authors did not evaluate how well LDA has performed in their experimentation. Additionally, they have used trial and error to determine the number of K topics that does not efficiently demonstrate the optimal number of K topics. It is worth mentioning that the literature review findings showed that some of the studies found determined their number of topics through trial and error during experimentation. [PERSON] and [PERSON] [40] evidenced the need to select an optimal number of topics as this enhances predicting ability, high isolation within topics, repeatability, and no duplicated topics. Thus, an efficient approach to determining the optimal number of topics should be adopted. More specifically, [PERSON] [41] extracted topics from Bulgarian bank chat data collected between January 2019 and April 2020. The author utilised cleaned 12,439 chats represented by a term frequency and inverse document frequency (TF-IDF) matrix to fit LDA. The study produced six coherent topics with a coherence score of 0.66. The result produced was used to understand the main themes that customers discussed, and the themes were profiled as loans, general information, digital banking, currency operations, identification, and cards/transfer. Despite the success achieved by LDA over the years, the unsupervised model has been criticized as it generates topics that contain irrelevant features and noisy topics [42], especially when dealing with short text such as social media text.