# A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care
## 5 Conclusions  
In this study, we focused on the use of TM methods for the Italian language. A methodology based on pre-processing implemented partly in Matlab and partly in Python has been proposed. The pre-processing involved a lemmatization step, which is particularly complicated for the Italian language.  
The implementation of four TM algorithms--namely, LDA, CTM, NMF, and Neural-LDA models--was performed using the OCTIS Python library, which allowed to conduct automatic optimization through the Single-Objective Bayesian Optimization. Performances of the TM algorithms were evaluated using two metrics based on the rank-biased overlap: Topic Diversity and Similarity. The goal was to obtain an optimal configuration that would guarantee good coherence between the k-words of the identified topics and, at the same time, guarantee an adequate degree of diversity. Three tests relating to the TM were performed, based on the identification of several topics (\(k\)) equal to 10, 20, and 25, from which it emerged that the optimal configuration was the one based on the LDA method and the use of a \(k\) equal to \(k=20\). The similarity value turned out to be optimal (with a value equal to 0.5) while guaranteeing an acceptable diversity value (about 0.2) at the same time. The results were presented graphically using Topic Visualization tools. In particular, the Topic Distribution, which highlights the percentage of belonging of a given conversation to a specific topic among those identified. Furthermore, word clouds were created to highlight the words with the greatest weight within a given topic.  
The main limitation of the present study is related to the difficulty of generalizing findings from our case study to other settings. Case studies are performed in real-world settings and, consequently, they have a high degree of realism, mainly at the expense of the level of control. Despite the fact that the case study of the present work may not generate the same insights as controlled experiments do, our study may provide an understanding of the best-performing TM algorithm. The research result can be exploited by practitioners to find the best approach for topic modeling of human-to-human conversation transcriptions. Therefore, this work can be an asset for grounding applications of topic modeling and can be inspiring for similar case studies in the domain of customer care quality.  
A possible extension of the current study could involve further investigation of the data analyzed. In particular, it would be useful to investigate methods that allow the exploitation of voice sources in real-time and integrated data from Web and Social sources.  
Conceptualization, M.P. ([PERSON]); methodology, M.P. ([PERSON]); software, M.P. ([PERSON]); validation, M.P. ([PERSON]) and G.P.; resources, G.P. and V.G.; writing--original draft preparation, G.P.; writing--review and editing, M.P. ([PERSON]), M.P. ([PERSON]), G.P.; supervision, G.P. and M.P. ([PERSON]); project administration, G.P. and V.G.; and funding acquisition, G.P. and V.G. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by Puglia Region (Italy)--Project \"VOice Intelligence for Customer Experience (VO.I.C.E. First)\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The actual transcriptions from the customer support center are not publicly available due to privacy concerns. The list of stopping words for algorithm implementation of the Italian natural language is available on request as a supplement to the present paper.  
**Acknowledgments:** The authors are thankful to Teleperformance S.p.A. (Italy) for providing the dataset for the case study.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.