## 1 Introduction  
Due to the increasing availability of big data, the need for topic modelling (TM) approaches for topic discovery is increasing [1]. For example, service industries such as banks sell different products and services. Thus, they need the main topics of their customers' enquiries, feedback, reviews, and discussions to help them target their resources. [PERSON] et al. [2] identified topic modelling as a type of statistical learning approach for detecting coherent topics in a document. Topic modelling (TM) is considered an important task for many applications such as aspect extraction in sentiment analysis [3; 4; 5], topic extraction for user preference in a recommender system [6], document summarization [7], topic discovery in a chat box system [8], and topic extraction for fake news detection [9]. TM has been applied successfully in different domains such as health [10], online learning platforms [11], software engineering [12], and legal documents [13] to discover hidden topics. With the increasing use of supervised learning algorithms to provide automated systems, TM provides an opportunity to apply unsupervised learning algorithms for topic discovery. This approach is beneficial because it limits the use of a labelled dataset for training purposes, which is labour intensive, time consuming, and expensive to obtain. There are popular topic modelling approaches, namely Latent Semantic Indexing (LSI), Probabilistic Latent Semantic Indexing (pLSI), and Latent Dirichlet Allocation (LDA). These models, especially LDA, have shown good performances over the years and several variants have been developed. Examples include the correlated topic model [14] and hierarchical Dirichlet process model [15]. However, these traditional TM approaches have been criticised for not being able to handle data sparsity, which is especially prevalent in short text. The Bi-Term Model (BTM) was developed to address this problem. This model has received prize in terms of performance when applied to short text. This is because BTM captures biterms from the whole corpus and, thus, the global co-occurrences of wordsare captured. The model assumes a pair of words can reveal topics much better than a single word occurrence. For example, biterms such as ATM_card and ATM_network are compared to unigrams such as card and network. However, [PERSON] et al. [16] stated BTM does not consider the comprehensive semantic dependencies of words, resulting in no contextual semantic. BTM has been criticised for the strong assumption made (that two co-occurring words will be assigned the same topic label) and this limits the performance. Thus, variants of this model have also been proposed such as Twitter-BTM [17] and GraphBTM [18]. More recently, the use of transformer-based language models has been proposed for TM. For example, BERT--Bidirectional Encoder Representations from Transformer [19]. However, these approaches are complex to use. Despite the use of topic modelling becoming more popular, the examples in the literature found to have compared the TM techniques are very limited. In addition, there is no study found to have applied TM techniques in the Nigerian banking context. The banking industry is an important sector for every nation's economy and banking is considered a daily activity in the society [20]. The Nigerian banking sector heavily relies on the cash-based economy and most buying and selling are performed with physical cash [21]. Thus, the banks can benefit from topic modelling by helping them monitor their customers' preferences towards their product and service. In addition, this will help them understand what their customers are talking about. Although traditional approaches have shown good performance, their performance is inconsistent. Thus, there is a need to conduct further experimental comparative studies of TM approaches to validate state-of-the-art TM models. To this end, this study aims to compare topic modelling techniques for topic extraction in the Nigeria banking context.