Extraction of Socio-semantic Data from Chat Conversations in Collaborative Learning Communities

Abstract

Online collaboration among communities of practice using text-based tools, such as instant messaging, forums and web logs (blogs), has become very popular in the last years, but it is difficult to automatically analyze all their content due to the problems of natural language understanding software. However, useful socio-semantic data can be retrieved from a chat conversation using ontology-based text mining techniques. In this paper, a novel approach for detecting several kinds of semantic data from a chat conversation is presented. This method uses a combination of a dialogistic, socio-cultural perspective and of classical knowledge-based text processing methods. Lexical and domain ontologies are used. A tool has been developed for the discovery of the most important topics and of the contribution of each participant in the conversation. The system also discovers new, implicit references among the utterances of the chat in order to offer a multi-voiced representation of the conversation. The application offers a panel for visualizing the threading of the subjects in the chat and the contributions function. The system was experimented on chat sessions of small groups of students participating in courses on Human-Computer Interaction and Natural Language Processing in ”Politehnica” University of Bucharest, Romania.