Semi-supervised Discovery of Informative Tweets During the Emerging Disasters
Table 2: Summary results to compare Semi-supervised (SS) and BOW algorithms.
['[BOLD] Train', '[BOLD] SH  [BOLD] SS∗', '[BOLD] SH  [BOLD] BOW∗', '[BOLD] BB  [BOLD] SS∗', '[BOLD] BB  [BOLD] BOW∗', '[BOLD] OT  [BOLD] SS∗', '[BOLD] OT  [BOLD] BOW∗']
Are word clusters better than BOW? Notice that for BOW approach at a specific training size, we report the best accuracy among different choices of K, which means we are treating the K as a hyperparameter during training. For the 7 semi-supervised approaches, we report the best accuracy from that group. When the training data size is 20, 50, and 100, the word clustering approach is superior to BOW on all but the Oklahoma Tornado (OT) data set. This result strongly indicates that an unlabeled Twitter corpus could provide a very useful information when there is a severe deficit of labeled tweets for an emerging disaster. As the number of labeled tweets is exceeding 100, the benefit from the unlabeled corpus fades, to the extent that BOW is more accurate than the word clusters on all 6 disasters when the training data size reaches 1,000. This is an expected result, since such a large training data set is sufficient to identify the most informative disaster-related words.