Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
TABLE VII: Scores of the Above Models According to the Proposed ERDE%o Criterium and the Flatency Measure from [68].
['Model', '[ITALIC] p>', '[ITALIC] ERDE%20', '[ITALIC] ERDE%50', '[ITALIC] Flatency']
While the “Meta LR Wait” model is now scored equally bad in both criteria because it had to read all documents, the CNN scores now tend to be better than the ones obtained for the metadata models alone. Still, the best overall ERDE%o scores could be achieved by the same ensemble. The additional models with higher thresholds that were previously included to obtain a better ERDE5 score (namely fastText reddit with p>0.8 and Meta LR with p>0.55) now result in the worst overall ERDE%o scores next to the waiting model. This again indicates that especially optimizations of ERDE5 do not necessarily mean a better classification result.