Finding automatization opportunities for email interactions can have positive effects for several industries, especially in tasks such as reading, receiving, writing and responding emails, categorizing emails or even to prevent loss of productivity and financial loses by dealing with spam, or improve users' satisfaction; even improving automatic categorization systems can mitigate negative impacts on personal and organization performance. Furthermore, people who work in companies spend around 28 % of their time reading and answering emails. In this project we proposed a methodology based on NLP and Unsupervised Machine Learning to look for opportunities of automation arising from recurrent email patterns found in email texts. We intent to facilitate the linguistic analysis in order to retrieve interaction patterns that can trigger automation actions. We proposed CRISP-DM methodology that lays the groundwork for detection of automatization opportunities in tasks relates. We compared the unsupervised machine learning methods K-Means, DBSCAN, and HDBSCAN with four clustering metrics applied to the Enron e-mails dataset transformed into paragraph vectors and performed several experiments with Word Mover's Distance, Euclidean Distance, L2-Norm and Cosine Similarity. Although our process yielded limited results in the detection of email interactions, we found that DBSCAN combined with Euclidean Distance was the best method among all scores. This project also contributes to the parameterization literature of said clustering algorithms as well as showing which methods, distances and scores settings are relevant for unsupervised email mining
