## 4 Results  
Table 3 below presents the coherence scores of the topic modelling techniques. The LDA has a coherence score of 0.3919 (cv), \(-9.1174\) (umass), and produced interpretable terms as shown in Table 4 below. \begin{table}
\begin{tabular}{l l l} \hline \hline
**LDA** & \multicolumn{2}{c}{**Terms**} \\ \hline Topic 1 & customer, cenbank\_central, branch, time, month, people, guy, atm, even, week, still, \\  & day, service, call, complaint, nothing, customer\_care, money, customer\_service, thing \\ \hline Topic 2 & job, morgan, free, min, company, una, fossil\_fuel, ready, hey\_investment, gas\_coal, \\  & financing\_oil, earth\_stop, company\_nofossil, nofossilfuel, fargo\_jpmorgan, change, \\  & actually, head, woman, country \\ \hline Topic 3 & help, access, account, money, transaction, please, transfer, pls, yet, day, number, kindly, \\  & yesterday, back, today, issue, alert, need, airtime, fund \\ \hline Topic 4 & loan, help, veilleur, bbnaja, cenbank\_central, kindly\_follow, day, send\_direct, \\  & officialefcc, message\_details, monthly, thank, house, enable\_assist, salary, ph, \\  & advise\_appropriately, heritagebank, ey\_ole, force \\ \hline Topic 5 & card, charge, never, new, use, open, year, debit, later, small, fee, pay, toy, top, see, right, \\  & also, month, nyou, far \\ \hline Topic 6 & business, lagos, great, school, news, monetizable\_activity, world, \\  & additional\_information, soon, let, part, good, best, opportunity, today, deposit, wish, \\  & thanks, trump, event \\ \hline Topic 7 & dm, help, detail, thank, number, twitter, kindly, please, complaint, sorry, send, tweet, \\  & transaction, handle, account, information, send\_dm, channel, phone\_number, message \\ \hline Topic 8 & line, phone, support, ng, mobile, mtnng\_mtn, hope, food, consumer, government, \\  & international, buhari, sort, whatsapp, explanation, enable, lagos, foundation, industry, \\  & ubaat \\ \hline Topic 9 & always, poor, stop, financial, ng\_union, cool, youfirst, sport, business, service, finance, \\  & internet\_banking, premiercool, mind, tell, thing, daily, single, fast, vista\_intl \\ \hline Topic 10 & credit\_card, citi, bill, state, debt, payment, chase, omo\_iya, mastercard, nthank, ng, \\  & future, true, alat, abuja, pay, india, atm\_machine, child, product \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clustering algorithms. \begin{table}
\begin{tabular}{c l} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Experimental Set Up**} \\ \hline K-means Clustering & n\_cluster = 8, n\_init = 10, max\_iter = 300, init = ’k-means++’ \\ \hline Agglomerative Clustering & n\_cluster = 2, linkage = ’ward’, metric = ’Euclidean’ \\ \hline HDBSCAN & min\_cluster\_size = 10, metric = ’Euclidean’, \\  & cluster\_selection\_method = ’eon’, alpha = 1.0 \\ \hline DBSCAN & Eps = 0.30, min\_samples = 9, metric = ’Euclidean’ \\ \hline BIRCH & Threshold = 0.01, n\_cluster = 3 \\ \hline OPTICS & min\_samples = 10, Eps = 0.8, metric = ’minkowski’ \\ \hline Meanshift & max\_iter = 300, min\_bin\_freq = 1 \\ \hline Spectral Clustering & n\_cluster = 8, eigen\_solver = ’arpack’, assign\_labels = ’kmeans’, \\  & n\_init = 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model coherence score. The above result showed HDP has the highest coherence score (cv), by far, of 0.6347. However, HDP achieved the highest negative coherence score (umass) of \(-\)16.5378. The terms were investigated manually as shown in Table 5 below. The HDP terms contain some unwanted words such as _'la'_, _'aw'_, and _'v'_ despite the corpus filtering, cleaning, and pruning. In addition, we observed some terms were common amongst the topics. For example, the term _'help'_ appeared in eight different topics. Whilst LSI performed with a marginally lower coherence score (cv) with low quality topics as shown in Table 6 below.