Enhancing Social Presence in Online Environments Affective Modeling Approach

Abstract

In face to face communications, people often rely on nonverbal cues such as body language, facial expressions, gestures, physical proximity, and dress to communicate and establish relationships. Recently computer mediated communication became a popular way of interaction. Unfortunately nonverbal elements are normally absent in online communications. This thesis presents an affect recognition model that assesses the emotional states of online users from textual messages. The study is based on the Social Information Processing (SIP) theory argument that “when most nonverbal cues are unavailable, as is the case in text-based computer mediated communication, users adapt their language, style, and other cues to such purposes”. The focus is on emotion recognition from online nonverbal textual symbols/patterns of vocalics (e.g. the use of capitals and use of punctuation “!” and “!!s!” or “?” and “???”, length of response e.t.c), and those of chronemics (e.g. time to respond to an email or to a discussion posting or a reply to a chat message e.t.c) that are used in text. The model uses Naïve Bayes classifier to recognize six basic emotions (anger, disgust, fear, happiness, sadness and surprise). The training set was developed based on the results of an online study named “Emotion Recognition from Nonverbal Symbols for Enhancing Social presence in Online Environments”, that was carried out to determine the l evel of use and the meaning of various nonverbal textual symbols used by students during their online communications. Two sets of training data were prepared, a dictionary of words associated with different categories of mentioned emotions and messages labeled with the six basic emotions collected from student’s online chats and posts. The messages were annotated by three student raters independently, the level of agreement was measured using Fleiss Kappa (k), and the reliability of agreement among the raters was moderate with a k of (0.7). Results of a user study comparing a chat system integrated with the affect recognition model with a conventional chat system suggest that an online interface that conveys emotional information helps online users to interact with each other more efficiently thus providing an enhanced social presence.