A Piece of My Mind: A Sentiment Analysis Approach for Online Dispute Detection
This addresses the problem of automatically detecting disputes in online discussions for moderators or researchers, though it appears incremental as it applies existing sentiment analysis methods to a new task.
The paper tackles online dispute detection by proposing a sentiment analysis approach that uses sentence-level sentiments as features in a classifier to predict dispute labels for discussions, achieving an F1 score of 0.78 and accuracy of 0.80 on a new Wikipedia Talk page corpus.
We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80.