Location reference identification from tweets during emergencies: A deep learning approach
Table 4: Result of the 2-CNN and 2-Dense with dropout model with combinations of 2-, 3-, 4-, and 5-gram filter size
['Approach', 'Filter size', 'Precision', 'Recall', 'F1-score', 'Hamming loss', 'Jaccard similarity', 'Exact matching']
We started experimentation with the different models: (i) 1-CNN + 2-Dense; (ii) 1-CNN + 2-Dense with Dropout; (iii) 2-CNN + 2-Dense; and (iv) 2-CNN + 2-Dense with Dropout. We used different combinations of 2-gram, 3-gram, 4-gram, and 5-gram filters with each of the models to extract the features from the tweet. The best-performing model was found to be 2-CNN + 2-Dense with dropout. In the analysis, we found that the use of a single filter size was not adequate as all the models performed very badly. The use of the two filters together performed better than the individual filters. The best result was achieved when we used 2-gram, 3-gram, and 4-gram filters together, which yielded the precision of 0.97, a recall of 0.93, an F1-score of 0.95, a hamming loss of 0.002, a Jaccard similarity of 0.953, and an exact matching score of 0.924. So, for all further experiments, we fixed the filter sizes of 2-gram, 3-gram, and 4-gram and used them together.