Recognition of Sentiment Sequences in Online Discussions

Abstract

Currently 19%-28% of Internet users participate in online health discussions. In this work, we study sentiments expressed on online medical forums. As well as considering the predominant sentiments expressed in individual posts, we analyze sequences of sentiments in online discussions. Individual posts are classified into one of the five categories encouragement, gratitude, confusion, facts, and endorsement. 1438 messages from 130 threads were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 and 0.763 for posts in sequence and separate posts respectively). The annotated posts were used to analyse sentiments in consecutive posts. In automated sentiment classification, we applied HealthAffect, a domain-specific lexicon of affective words.