Suicidal Ideation and Mental Disorder Detectionwith Attentive Relation Networks
Table 8: Performance comparison on Twitter dataset, where precision, recall, and F1 score are weighted average.
['Model', 'Accuracy', 'Precision', 'Recall', 'F1']
Twitter Collection Lastly, we conduct experiments on the Twitter dataset with similar settings of previous experiments. Unlike posts in Reddit, tweets in this dataset are short sequences due to the tweet’s length limit of 280 characters. Among these competitive methods, our model gains the best performance on these four metrics, with 1.77% and 1.82% improvement than the second best BiLSTM model in terms of accuracy and F1-score respectively. Our proposed method introduces auxiliary information of lexicon-based sentiment and topics features learnt from corpus, and utilizes RNs for modeling the interaction between LSTM-based text encodings and risk indicators. Richer auxiliary information and efficient relational encoding help our model boost performance in short tweet classification.