Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds
TABLE V: Sentiment Analysis for the Presidential Debates: f1 stands for 1-gram, f2 stands for 2-gram, f3 stands for (1+2)-gram, f4 stands for (1+2)-gram+MISC, f5 stands for DOC, f6 stands for DOC+TOPIC.
['[BOLD] Method', '[BOLD] Hamming Loss  [ITALIC] f1', '[BOLD] Hamming Loss  [ITALIC] f2', '[BOLD] Hamming Loss  [ITALIC] f3', '[BOLD] Hamming Loss  [ITALIC] f4', '[BOLD] Hamming Loss  [ITALIC] f5', '[BOLD] Hamming Loss  [ITALIC] f6']
We use 3 different datasets for the problem of sentiment analysis, as already mentioned. We use this metric because our problem is in the multi-class classification domain. However, the single-label classifiers like SVM, Naive Bayes, Elman RNN cannot be evaluated against this metric directly. To do this, we split the predicted labels into a format that is consistent with that of multi-label classifiers like RaKel. In the table, f1, f2, f3, f4, f5 and f6 denote the features 1-gram, 2-gram, (1+2)-gram, (1+2)-gram+MISC, DOC and DOC+TOPIC respectively. Note that lower values of hamming losses are more desirable.