The search for topics related to electric mobility: a comparative analysis of some of the most widely used methods in the literature

Identifying the topics addressed in a corpus is one of the primary concerns of automated text analysis. This paper aims to contribute to the comparative analysis of various methodologies. Specifically, a comparison is made of the results obtained by applying the most prevalent topic identification techniques to the same corpus. The analysis is conducted on a large database of original text created from an e-mobility newsletter. To evaluate the outcomes of the methodologies, two criteria are used. First, the semantic coherence and similarities of the various methods are assessed. The second step involves processing the degree of association between the topics identified by the various models.