[1] Depression and other common mental disorders: Global health estimates. World Health Organization[EB/OL]https://apps.who.int/iris/bitstream/handle/10665/254610/WHO-MSD-MER-2017.2-eng.pdf [2021-06-28]
[2] HUSSAIN J, SATTI F A, AFZAL M, et al. Exploring the dominant features of social media for depression detection[J]. Journal of Information Science, 2020, 46(6): 739-759.
[3] TADESSE MM, LIN H, XU B,et al. Detection of depression-related posts in reddit social media forum[J]. IEEE Access, 2019(7): 44883-44893.
[4] PARK M, CHA C, CHA M. Depressive moods of users portrayed in twitter[A]. ACM SIGKDD Workshop on Healthcare Informatics. Beijing, China, 2012.
[5] TSUGAWA S, KIKUCHI Y, KISHINO F, et al. Recognizing depression from twitter activity[C]//Proceedings of the 33rd annual ACM Conference on Human Factors in Computing Systems, 2015: 3187-3196.
[6] YAZDAVAR A H, Al-OLIMAT H SEBRAHIMI M, et al. Semi-supervised approach to monitoring clinical depressive symptoms in social media[C]//Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017: 1191-1198.
[7] OGUZHAN T, FARZAD K. A survey automatic text summarization[M]. Press Academia Procedia, 2007: 205-213.
[8] GAUR M, ALAMBO A, SAIN J P, et al. Knowledge-aware assessment of severity of suicide risk for early intervention[C]//Proceedings of the World Wide Web Conference, 2019: 514-525.
[9] CAO L, ZHANG H, FENG L, et al. Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered attention[J]. arXiv preprint arXiv: 1910.12038, 2019.
[10] CHOUDHURY M D, GAMON M, COUNTS S,et al. Predicting depression via social media[C]//Proceedings of the International AAAI Conference on Web and Social Media, 2013.
[11] TSUGAWA S, KIKUCHI Y, KISHINO F,et al. Recognizing depression from twitter activity[C]//Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 2015: 3187-3196.
[12] ORABI A H, BUDDHITHA P, ORABI M H, et al. Deep learning for depression detection of Twitter users[C]//Proceedings of the 5th Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 2018: 88-97.
[13] 査猛,叶宁,王汝传,等.基于胶囊网络模型的抑郁症预测研究[J].计算机技术与发展,2021,31(11): 28-34.
[14] 鲁小勇,石代敏,刘阳,等.注意力残差模型的语音抑郁倾向识别方法[J].小型微型计算机系统,2022,43(08): 1602-1608.
[15] MURARKA A, RADHAKRISHNAN B, RAVICHANDRAN S. Detection and classification of mental illnesses on social media using RoBERTa[J]. arXiv preprint arXiv: 2011.11226, 2020.
[16] ZHOU D, ZHANG X, ZHOU Y, et al. Emotion distribution learning from texts[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016: 638-647.
[17] 周瑛,刘越,蔡俊.基于注意力机制的微博情感分析[J].情报理论与实践,2018,41(03): 89-94.
[18] ARAGN M E, et al. Detecting depression in social media using fine-grained emotions[C]//Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019.
[19] ARAGON M E, LOPEZ-MONROY A P, GONZALEZ-GURROLA L C G, et al. Detecting mental disorders in social media through emotional patterns: The case of anorexia and depression[J]. IEEE Transactions on Affective Computing, 2021, 14(1): 211-222.
[20] LARA J S, ARAGN M E, GONZLEZ F A, et al. Deep bag-of-sub-emotions for depression detection in social media[C]//Proceedings of the International Conference on Text, Speech, and Dialogue. Springer, Cham, 2021: 60-72.
[21] SALIMINEN J, HOPF M, CHOWDHURY S A, et al. Developing an online hate classifier for multiple social media platforms[J]. Human-centric Computing and Information Sciences, 2020, 10: 1-34.
[22] DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv: 1810.04805, 2018.
[23] LIU P. NULI At SemEval Task 6: Transfer learning for ofensive language detection using bidirectional transformers[C]//Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, 2019: 87-91.
[24] NIKOLOV A, RADIVCHEV V. SemEval Task 6: Ofensive tweet classifcation with BERT and ensembles[C]//Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, 2019: 691-695.
[25] FRIEDRICH M. Depression is the leading cause of disability around the world[J]. JAMA, 2017, 317(15): 1517-1518.
[26] MOHAMMAD S, TURNEY P. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon[C]//Proceedings of the NAACL HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 2010: 26-34.
[27] CUI Y, JIA M, LIN T Y, et al. Class-balanced loss based on effective number of samples[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 9268-9277.
[28] LOSADA D E, CRESTANI F. A test collection for research on depression and language use[C]//Proceedings of the International Conference of the Cross-Language Evaluation Forum for European Languages. Springer, Cham, 2016: 28-39.
[29] LOSADA D, CRESTANI F, PARAPAR J. Overview of erisk: Early risk prediction on the internet[C]//Proceedings of the 9th International Conference of the CLEF Association, Avignon, France, 2018.
[30] VAPNIK V, GUYON I, HASTIE T. Support vector machines[J]. Mach Learn, 1995, 20(3): 273-297.
[31] KALCHBRENNER N, GREFENSTETTE E, BLUNSOM P. A convolutional neural network for modelling sentences[J]. arXiv preprint arXiv: 1404.2188, 2014.
[32] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Comput, 1997,9(8): 1735-1780.
[33] GRAVES A, JAITLY N, MOHAMED A. Hybrid speech recognition with deep bidirectional LSTM[C]//Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, IEEE, 2013: 273-278.
[34] YAN Y, LIU F, ZHUANG X, et al. An r-transformer_BiLSTM model based on attention for multi-label text classification[J]. Neural Processing Letters, 2022: 1-24.
[35] ANSARI L, JI S, CHEN Q, et al. Ensemble hybrid learning methods for automated depression detection[J]. IEEE Transactions on Computational Social Systems, 2022, 10(1): 211-219.
[36] KOTENKO I, YASH S, ALEXANDER B. Predicting the mental state of the social network users based on the latent dirichlet allocation and fastText[C]//Proceedings of the 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IEEE, 2021.