Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
Table 5: Performance comparison for different word embedding and different detection model, where “So-W2v”, “So-Glove” and “So-FastText” represent suicide-oriented word embeddings based on Word2vec, GloVe and FastText respectively. Acc and F1 represent accuracy and F1-score.
['LSTM', 'Acc(%)', 'Word2vec 79.21', 'GloVe 80.17', 'FastText 82.59', 'Bert 85.15', 'So-W2v 86.00', 'So-GloVe 86.45', 'So-FastText  [BOLD] 88.00']
5.4.1 Effectiveness of Suicide-oriented Word Embeddings We find that without suicide-related dictionary, Bert outperforms other three word embeddings with 2% higher accuracy and 1.5% higher F1-score on both models. After leveraging suicide-related dictionary, suicide-oriented word embeddings based on FastText achieves the best performance with accuracy 88.00% 91.33%, F1-score 88.14%, 90.92% on two models. Obviously, there is a gap between suicide-oriented word embeddings and normal word embeddings which can verify the effectiveness of the former.