Process models of interrelated speech intentions from online health-related conversations

Publication date: Available online 18 July 2018Source: Artificial Intelligence in MedicineAuthor(s): Elena V. Epure, Dario Compagno, Camille Salinesi, Rébecca Deneckere, Marko Bajec, Slavko ŽitnikAbstractBeing related to the adoption of new beliefs, attitudes and, ultimately, behaviors, analyzing online communication is of utmost importance for medicine. Multiple health care, academic communities, such as information seeking and dissemination and persuasive technologies, acknowledge this need. However, in order to obtain understanding, a relevant way to model online communication for the study of behavior is required. In this paper, we propose an automatic method to reveal process models of interrelated speech intentions from conversations. Specifically, a domain-independent taxonomy of speech intentions is adopted, an annotated corpus of Reddit conversations is released, supervised classifiers for speech intention prediction from utterances are trained and assessed using 10-fold cross validation (multi-class, one-versus-all and multi-label setups) and an approach to transform conversations into well-defined, representative logs of verbal behavior, needed by process mining techniques, is designed. The experimental results show that: (1) the automatic classification of intentions is feasible (with Kappa scores varying between 0.52 and 1); (2) predicting pairs of intentions, also known as adjacency pairs, or including more utterances from even other heterogeneous corpora can ...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research