## 4 Findings from the Literature Review  
After discussing the application steps of the LDA, this section of the paper will go into detail about the evidence found in the literature review. Beyond this, the results obtained by applying LDA will be evaluated, defining whether the categorization of contributions obtained corresponds to the real content of the papers. The analysis carried out with the LDA led to the definition of four clusters within the analyzed thematic area. This value was defined because of the highest coherence score obtained. As explained above, each contribution was assessed, resulting in a value being assigned to each of the four identified topics. Each paper was attributed to the topic with higher membership value. The graphical display of the topics, following the proposal in [27], represents how the application of the LDA clustered the thematic area. Each bubble represents a topic, considering that the bigger the bubble, the more prevalent the topic. This mean that in Figure 6 the size of the bubbles represents, for the 30 most frequent termsderived from the linguistic analysis, how relevant (in complexity) the specific topics are in the definition of these terms. A model is considered effective when the bubbles are fairly big, non-overlapping and scattered throughout the chart instead of being clustered in one quadrant. To be more specific, the identified topics are plotted in a two-dimensional space, where the centers are determined by calculating the distances between the topics. The inter-topic distances are then projected using multidimensional scaling onto a two-dimensional space [28]. The authors decided to utilize the default settings [27], which are Jensen-Shannon divergence for computing inter-topic distances and Principal Components for scaling the set of inter-topic distances. The clustering of the identified topics is clear and well-defined, but the identified thematic sub-areas do not have points in common. From this initial observation it can be deduced that, according to the logic of the LDA, the terminology makes it easy to identify specific thematic areas, which certainly have their own peculiarities, but also points of contact with other topics. At first glance, it can already be seen that, apart from topic 2 which has the most contributions, the other three topics has similar size. All acronyms are detailed in Appendix A, so as not to create difficulties in reading the paper. The next step check how the selected keywords describe areas of interest, also considering the coherence between them and the papers labeled with them. A first comparison shows that some keywords are present in several clusters, indicating thematic transversality. An obvious example is that of the keywords \"diagnosis\", \"detect\", \"pattern\", and \"extract\". These keywords are objective, in the sense of the objective being pursued in the research in which they are encountered. This plurality should not come as a surprise, as it is quite evident that these activities occur in various fields of application and with different techniques. In general, considering also that each paper is allocated to each of the tasks with a specific percentage of membership, it is important to consider that the allocations are indicative of the topic that is mostly contained in that contribution. However, some contributions present transversal themes and have been allocated to a specific contribution either because of the connection with the other themes of the selected topic or because  
Figure 6: Topics distribution. that particular thematic area is predominant in it. In general, therefore, this clustering cannot be considered watertight compartmentalization since the various thematic areas talk to each other. Another point of attention is the application context. For example, the term \"bear\" (the root of the word \"bearing\") is present in three clusters, just as the term gearbox is present in two.