Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon
Table 5: Results on Wikipedia talk page (AAWD) (with soft F1 score) and online debate (IAC) with different feature sets (i.e Lexical, Syntacitc/Semantic, Discourse, Conversation, and Sentiment features) by using isotonic CRF. The numbers in bold are statistically significantly higher than the numbers above it (paired-t test, p<0.05).
['[BOLD] IAC', '[BOLD] Agree', '[BOLD] Disagree', '[BOLD] Neu']
Moreover, we evaluate the effectiveness of features by adding one type of features each time. As it can be seen, the performance gets improved incrementally with every new set of features.