Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
Table 6: Performance comparison between different suicide risk detection model, where Acc and F1 represent accuracy and F1-score respectively.
['[EMPTY]', 'Full testset Acc', 'Full testset F1', 'Harder sub-testset Acc', 'Harder sub-testset F1']
In this case, LSTM and SDM employs So-FastText word embeddings as their input. SDM improves the accuracy by over 3.33% and obtains 2.78% higher F1-score on full data set.