Scopus is a well known repository of metadata about scientific research articles. In this work, we gather data from this repository to create a social graph of scientific authors, starting from citations among their articles. Moreover, using data mining techniques, we infer some relevant research topics for each author, from the textual analysis of the abstracts of his articles. As a case study, we have limited our research to the authors who have published at least one article about Sentiment Analysis, in a decade. Starting from the more relevant terms extracted from abstracts, we then perform a clusterization of users. This shows the emergence of some subtopics of Sentiment Analysis, which are studied by distinct groups of authors.