## 1 Introduction  
In computer science, natural language processing (NLP) is a research domain that deals with semantic mining, enabling computers to obtain meaning from human language [1]. Topic modeling (TM) is an area of research for the scientific community of NPL. Generally, NLP includes machine translation, content extraction, question answering, information retrieval, and text generation [2]. Furthermore, NLP includes text classification, the grouping of documents based on similar characteristics and contents, concepts/topics detection and extraction, sentiment analysis, and text summarization. TM is a text mining approach to address the problem of grouping documents based on their topic and similarities by automatically finding patterns and characteristics from the data itself without any predefined data labels. With its capability, TM enables understanding of the collection of documents as well as the building of a robust search engine [3]. TM finds hidden subjects in the collection and describes the relationships between them and each document. TM clusters the documents and indicates the contents of each cluster simultaneously. Several practical TM techniques were proposed in previous research, including probabilistic latent semantic analysis (PLSA) [4], non-negative matrix factorization (NMF) [5], latent Dirichlet allocation (LDA) [6] and structural topic modeling (STM) [7]. In recent years, the studies on the application of TM methods were also active, for example, in political science, bio-informatic, healthcare, and medicine [8; 9; 10; 11; 7]. In a customer support center, the LDA algorithm enables the modeling, of conversation transcriptions, between customers and agents, as topical mixtures. Each topic is a multinomial distribution over problems and complaints discussed in the conversation. LDA interprets each transcription as an unordered bag-of-words, and a transcription is a probabilistic realization of a mixture model over customer problems and complaints. Through the LDA algorithm, the analysis of transcriptions can provide insights into the quality of the service. \"Perplexity\" is an example of a metric that allows accurate evaluation of LDA results and the model generalization ability likelihood on unknown data [12; 13]. Besides the used approach to analyze the text documents, given the amount of data available, making the mining activity automatic is the natural requirement of any actual application. The possible constant use of the human control of the activities would involve not fully exploiting the potentialities of the mining techniques. Text mining is a multi-step process that requires specific configurations and the choice of parameters for each step of the analysis. Hence, some kind of expertise is required to guide the analysis process. Solutions for investigating a large set of transcriptions, without supervision by human analysts and data experts, may be of practical value in real applications. Until now, most of the representative text pre-processing tools and language models have been based on English. Thus, the majority of data sources used in text-based research are also documented in English. Current NLP methods and algorithms mainly focus on several high-resource languages, such as English, Chinese, Japanese, Korean, German and French. Low-resource languages, such as Italian, were underserved by NLP systems. That is mainly due to the lack of datasets and support for the specific language. Furthermore, English approaches cannot be extended to other languages because the dictionary is not the same, and each language requires its own lexicon database [14]. There are several crucial challenges in the multi-lingual and cross-lingual development of NLP models [1]. Therefore, it is essential to develop research projects at the country level to create NLP applications for domestic languages. An example related to a low-resource language, such as the Persian natural language, can be found in [15].