# A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care
## 3 Data Pre-Processing  
After defining the TM algorithms included in the comparative study of this paper, it is necessary to investigate the data pre-processing techniques necessary to make the dataset more usable by the TM method. In particular, our process is based on techniques that allow the analysis of the Italian natural language. Generally, after the first step of data acquisition, the main pre-processing phases are defined as follows:  
* Tokenization: a process of breaking down captured text data into words, called tokens. One of the most used tools in computer science to perform tokenization is the python library Natural Language Toolkit (NLTK).
* Lowercasing: the process of converting each word to its lowercase (WORD \(\rightarrow\) word).
* Stopwords and punctuation removal: the phase in which the stopwords, i.e., meaningless terms such as articles and propositions, and punctuation are removed. In this way, it is possible to lighten the dataset from all those superfluous elements to evaluate the topics.
* Stemming: truncation of words at the root, omitting endings that indicate, for example, gender, number; alterations for nouns and adjectives; or mood, tense, or person for verbs. This makes sense if the root of the word is sufficiently representative of the meaning without the risk of ambiguity.
* Lemming: reduction of the word to a more canonical form--to be used as an alternative to stemming when you want to achieve maximum convergence of words--towards a common lemma that can grasp its meaning. Lemmatization operates at a considerably higher level of complexity than stemming. This phase turns out to be one of the most complex to implement for the Italian language, as there is no Python library capable of performing a correct lemmatization for the natural Italian language.  
To better understand the effectiveness of the lemmatization process by comparing it with stemming, it is useful to report an example relating to one of the transcripts present in the dataset used in the case study. For example, conversation number 1 contains the term \"soliccitato\" (the Italian word for \"hasten\"). The result of the stemming process, carried out initially, was the following:_Stemming_  
\"solicetato\" \(\rightarrow\) \"solicet\"  
\"solicetato\", \"solicetate\" \(\rightarrow\) \"solicet\"  
Below is the output of the lemmatization process for the word \"solicetare\"  
_Lemming_  
\"solicetato\" \(\rightarrow\) \"solicetation\"  
In some cases, lemmatization can produce a verb in the infinitive form as a result of a word canceling the conjugation. The phase following the pre-processing is the creation of the BoW. It consists of the definition of the vocabulary that, using a weight for each word, allows the identification of the topics. The BoW is similar to a list of words without any order and grammatical rules. The main challenge faced during the pre-processing phase was the application of the methods mentioned above for the Italian natural language. The approach to face this problem is described in more detail in the subsequent section.