Text Mining, Affective Computing and Behavioral Analysis

This project aims to extract knowledge from different unstructured sources. In text mining, information is usually obtained by identifying patterns and trends through statistical or machine learning from text. In addition to the meaning of the words and the main message passed in writing, the text produced brings different information from the emotions of the one who is writing it. In this way, the objective in this area is to represent texts in the vector space, to develop classification algorithms and to build applications focused on the analysis of feelings.

In this context, affective computing establishes itself as emotion-related computing, coming from emotions or deliberately influencing emotions. This area has many open challenges, especially in the area of emotion detection and classification. It comprises the study and development of systems that can recognize, interpret, process, and simulate human affection. The objective of this project is to develop and use devices capable of recognizing facial expressions, gestures, speech, changes in body temperature, respiratory rhythm, among others, as well as extracting significant patterns of these captured data.

Behavioral analysis is the analysis of individuals in groups, be it in social networks, networks of collaboration and co-authorship. The objective of this project is to analyze the behavior of individuals in their groups or communities modeled through graphs with attributes so that relevant communities can be evidenced. In this research, we make use of different approaches, such as algorithms of grouping in graphs with attributes for the detection of communities. Among the applications studied is the detection of groups for targeted marketing, detection of emotional homophily and dissemination of information.

Faculty Involved

  • Eduardo Bezerra
  • Gustavo Guedes (Leader)
  • Kele Belloze

Comments are closed.