Unifying Local and Global Agreement and Disagreement Classification in Online Debates
Jie Yin, Nalin Narang, Paul Thomas, Cécile L. Paris · 2012
Online debate forums provide a powerful communication platform for individual users to share information, exchange ideas and ex-press opinions on a variety of topics. Under-standing people’s opinions in such forums is an important task as its results can be used in many ways. It is, however, a challeng-ing task because of the informal language use and the dynamic nature of online conversa-tions. In this paper, we propose a new method for identifying participants ’ agreement or dis-agreement on an issue by exploiting infor-mation contained in each of the posts. Our proposed method first regards each post in its local context, then aggregates posts to es-timate a participant’s overall position. We have explored the use of sentiment, emotional and durational features to improve the accu-racy of automatic agreement and disagree-ment classification. Our experimental results have shown that aggregating local positions over posts yields better performance than non-aggregation baselines when identifying users’ global positions on an issue. 1