## 2 Topic Modeling  
The goal of TM is to define the topics within a data set of transcriptions, of which there is no a priori information; the output of TM is a set of clusters, that is, homogeneous groups of transcriptions that deal with the same topic. The models can be distinguished based on the ability to manage the presence of multiple themes within the transcription: some models handle these documents as outliers by excluding them from all the clusters formed; other models represent the co-presence as a probability distribution of more topics in the transcription. Within the reference case study of our study, it is not unusual to deal with multitopic conversation transcriptions between customers and agents and topics overlapping in the customers' support requests.  
The basic concept of TM methods is taking the numeric (document \(\times\) term) vectors that represent the documents as input and converting them into (topic \(\times\) term) vectors and (document \(\times\) topic) vectors. Each input transcription is assigned to one topic group using the (document \(\times\) topic) matrix.  
How documents are represented numerically is one of the basic features of a TM method; in the bag-of-words paradigm, the most widely used approach, documents are a collection of unordered words. The \"term frequency\"-\"inverse document frequency\" is a widely-used technique for implementing a bag-of-words in TM by adding the importance of the term in the collection to the count vector.  
The first component is the \"term frequency\", which counts how many times each phrase appears in a document; the second component is the \"inverse document frequency\" which determines the extent to which a phrase is used throughout the text. However, using this representation, relationships between words are lost during text processing. To deal with this problem, in 2013, the research team at Google advocated a new approach, called Word2 Vec [28], which involves two-layer neural networks and embeds each word in a numeric vector space. The technique allows the semantic similarity of words to be calculated, and a document can be characterized by a vector incorporating the relationship between the words using the average values of Word2 Vec [29]. Such a technique was further developed and expanded to the document scale, which is called Doc2 Vec [30].  
Starting from the numerical representation in a vector space, TM methods can be categorized into two types of models: probabilistic and non-probabilistic. Examples of the first type of approach are the probabilistic latent semantic analysis (PLSA) [4] and the latent Dirichlet allocation (LDA) [6]. PLSA and LDA methods calculate the likelihood of a word appearing in several subjects, and the likelihood of the topics in the document. The non-negative matrix factorization (NMF) [5] and the latent semantic analysis (LSA) [31] are two non-probabilistic approaches. Both NMF and LSA are algebraic approaches using matrix factorization.  
Among the number of methods proposed in the literature, LDA and NMF are the most popular techniques for actual applications [32; 33; 34]; at the same time, using various data sets, researchers continue to investigate improved TM algorithms. Studies in [35; 36; 37] were conducted to compare the existing techniques explained above, and the conclusions show that the best methods are different depending on the data sets.  
Improvements in the LDA performance are provided by the embedded topic model (ETM), presented in [38]. The ETM is a document model that matches the original LDA to find an interpretable latent semantic format for the documents, and word embeddings to provide a low-dimensional representation of the meaning of words. For the present study, we refer to the original LDA algorithm since the objective is to develop a TM approach for unannotated corpora of transcriptions in an unsupervised learning framework.