## 3 Methodology
### Experimental Setup  
The dataset went through pre-processing. The natural language toolkit (NLTK) library [57] was used for tokenisation, removal of stop-words, word lemmatisation, and part of speech (POS) tagging. The POS were extracted using n-gram for n = 1, 2, 3. This means the unigram, bigram, and trigram were all considered. It is worth noting that steps such as word lemmatisation and POS tagging were applied to the English tokens in the dataset. The topic models were iterated to classify 10 topics and 20 terms per topic. The Gensim package [58] was used to implement the traditional topic models. The LDA model chunksize was set to 1740, with iterations at 1000 and passes at 20. Both alpha and beta Dirichlet priors were set to 'auto' to allow the model to automatically learn the best values for the hyperparameters during training. The LSI model chunksize was set to 1740 and the power iteration at 1000. The HDP model chunksize was set to 256, alpha at 1, and eta at 0.01. The BERTopic architecture [53] comprises the embedding, dimensionality reduction, clustering, and the C-TFIDF modules. In the embedding component, the BERT, SBERT [59], and FinBERT [60] were used to create word/document embedding. Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Isometric Mapping (ISOMAP), Singular Value Decomposition (SVD), and Uniform Manifold Approximation and Projection (UMAP) were used for dimensionality reduction. Table 1 below shows the parameter set up of the dimensionality reduction algorithms.  
For the third component, the clustering methods used were K-means, Spectral, Agglomerative, MeanShift, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), and Ordering Points to Identify the Clustering Structure (OPTICS). Table 2 below shows the parameter set up of the cluster analysis algorithms.  
For evaluation purposes, there is no agreed standard metric; however, the coherence score is used to justify the performance of the topic models. Topic coherence is a common evaluation metric for TM techniques and it is understood that the higher the coherence scores the better [61]. [PERSON] et al. [62] showed that the coherence score has the highest correlation with human judgements of topics. However, [PERSON] et al. [63] argued that evaluating TM methods using a coherence score only is not enough. This needs to be complemented with topic quality and interpretability. In addition, there is no standard performance cut off for the coherence score to determine the best performing topic model. In the next section, we present the results of the experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Experimental Set Up** \\ \hline ISOMAP & number of neighbours = 15, n\_components = 5, eigen\_solver = ‘auto’ \\ \hline PCA & n\_components = 5 \\ \hline KPCA & n\_components = 5, kernel = ‘radial basis function’, gamma = 15, random\_state = 42 \\ \hline SVD & n\_components = 5, algorithm = ‘randomized’, random\_state = 0 \\ \hline UMAP & number of neighbours = 15, n\_components = 10, min\_dist = 0.0, metric = ‘cosine’ \\ \hline \end{tabular}
\end{table}
Table 1: Dimensionality reduction algorithms.