{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:15:23Z","timestamp":1791486923500,"version":"4.3.4"},"reference-count":86,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61632011, 61572102, 61602078,61572098"],"award-info":[{"award-number":["61632011, 61572102, 61602078,61572098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["DUT18ZD102"],"award-info":[{"award-number":["DUT18ZD102"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Education Humanities and Social Science Project","award":["19YJCZH199"],"award-info":[{"award-number":["19YJCZH199"]}]},{"name":"Foundation of State Key Laboratory of Cognitive Intelligence","award":["2018M631788, 2018M641691"],"award-info":[{"award-number":["2018M631788, 2018M641691"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.<\/jats:p>","DOI":"10.3390\/a13010007","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T10:28:43Z","timestamp":1577183323000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":217,"title":["Detection of Suicide Ideation in Social Media Forums Using Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7967-4651","authenticated-orcid":false,"given":"Michael Mesfin","family":"Tadesse","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongfei","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"},{"name":"State Key Laboratory of Cognitive Intelligence, iFLYTEK, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2018). National Suicide Prevention Strategies: Progress, Examples and Indicators, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1001\/jama.1975.03260240050026","article-title":"Hopelessness and suicidal behavior: An overview","volume":"234","author":"Beck","year":"1975","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1001\/archpsyc.1971.01750180093015","article-title":"Relation of depression of attempted suicide and seriousness of intent","volume":"25","author":"Silver","year":"1971","journal-title":"Arch. Gen. Psychiatry"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/sltb.12068","article-title":"Differentiating suicide attempters from suicide ideators: A critical frontier for suicidology research","volume":"44","author":"Klonsky","year":"2014","journal-title":"Suicide Life-Threat. Behav."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1111\/sltb.12051","article-title":"Sociodemographic and clinical differences between suicide ideators and attempters: A study of mood disordered patients 50 years and older","volume":"44","author":"Pompili","year":"2014","journal-title":"Suicide Life-Threat. Behav."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jad.2009.10.020","article-title":"Apples to oranges?: A direct comparison between suicide attempters and suicide completers","volume":"124","author":"DeJong","year":"2010","journal-title":"J. Affect. Disord."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., and Kumar, M. (2016, January 9\u201312). Discovering shifts to suicidal ideation from mental health content in social media. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jos\u00e9, CA, USA.","DOI":"10.1145\/2858036.2858207"},{"key":"ref_8","unstructured":"Marks, M. (2019). Artificial Intelligence Based Suicide Prediction. Yale J. Health Policy Law Ethics, Forthcoming."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kumar, M., Dredze, M., Coppersmith, G., and De Choudhury, M. (2015, January 4\u20137). Detecting changes in suicide content manifested in social media following celebrity suicides. Proceedings of the 26th ACM conference on Hypertext & Social Media, Prague, Czech Republic.","DOI":"10.1145\/2700171.2791026"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ji, S., Long, G., Pan, S., Zhu, T., Jiang, J., and Wang, S. (2019, January 22\u201325). Detecting Suicidal Ideation with Data Protection in Online Communities. Proceedings of the International Conference on Database Systems for Advanced Applications, Chiang Mai, Thailand.","DOI":"10.1007\/978-3-030-18590-9_17"},{"key":"ref_11","unstructured":"Yang, Y., Zheng, L., Zhang, J., Cui, Q., Li, Z., and Yu, P.S. (2018). TI-CNN: Convolutional neural networks for fake news detection. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mikolov, T., Karafi\u00e1t, M., Burget, L., \u010cernock\u1ef3, J., and Khudanpur, S. (2010, January 26\u201330). Recurrent neural network based language model. Proceedings of the Eleventh Annual Conference of the International Speech Communication Association, Makuhari, Chiba, Japan.","DOI":"10.21437\/Interspeech.2010-343"},{"key":"ref_13","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed representations of words and phrases and their compositionality. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Coppersmith, G., Ngo, K., Leary, R., and Wood, A. (2016, January 16). Exploratory analysis of social media prior to a suicide attempt. Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA.","DOI":"10.18653\/v1\/W16-0311"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1089\/cpb.2007.9999","article-title":"A suicide in an online mental health support group: Reactions of the group members, administrative responses, and recommendations","volume":"10","author":"Hsiung","year":"2007","journal-title":"CyberPsychol. Behav."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1027\/0227-5910\/a000234","article-title":"Tracking suicide risk factors through Twitter in the US","volume":"35","author":"Jashinsky","year":"2014","journal-title":"Crisis"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.comcom.2015.07.018","article-title":"Analysing the connectivity and communication of suicidal users on twitter","volume":"73","author":"Colombo","year":"2016","journal-title":"Comput. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1016\/j.socscimed.2009.07.041","article-title":"Copycat effects after media reports on suicide: A population-based ecologic study","volume":"69","author":"Niederkrotenthaler","year":"2009","journal-title":"Soc. Sci. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.socscimed.2017.06.032","article-title":"Tweeting celebrity suicides: Users\u2019 reaction to prominent suicide deaths on Twitter and subsequent increases in actual suicides","volume":"189","author":"Ueda","year":"2017","journal-title":"Soc. Sci. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6351","DOI":"10.1016\/j.eswa.2013.05.050","article-title":"Emotion detection in suicide notes","volume":"40","author":"Desmet","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, X., Zhang, L., Chiu, D., Liu, T., Li, X., and Zhu, T. (2014, January 9\u201312). Detecting suicidal ideation in Chinese microblogs with psychological lexicons. Proceedings of the 2014 IEEE 11th Intl. Conf. on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl. Conf. on Autonomic and Trusted Computing and 2014 IEEE 14th Intl. Conf. on Scalable Computing and Communications and Its Associated Workshops, Bali, Indonesia.","DOI":"10.1109\/UIC-ATC-ScalCom.2014.48"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Braithwaite, S.R., Giraud-Carrier, C., West, J., Barnes, M.D., and Hanson, C.L. (2016). Validating machine learning algorithms for Twitter data against established measures of suicidality. JMIR Ment. Health, 3.","DOI":"10.2196\/mental.4822"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jad.2014.08.047","article-title":"The association of suicide-related Twitter use with suicidal behaviour: A cross-sectional study of young internet users in Japan","volume":"170","author":"Sueki","year":"2015","journal-title":"J. Affect. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.invent.2015.03.005","article-title":"Detecting suicidality on Twitter","volume":"2","author":"Wan","year":"2015","journal-title":"Internet Interv."},{"key":"ref_25","unstructured":"Wood, A., Shiffman, J., Leary, R., and Coppersmith, G. (2016, January 7\u201312). Language signals preceding suicide attempts. Proceedings of the CHI 2016 Computing and Mental Health Workshop, San Jose, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Okhapkina, E., Okhapkin, V., and Kazarin, O. (2017, January 27\u201329). Adaptation of information retrieval methods for identifying of destructive informational influence in social networks. Proceedings of the 2017 IEEE 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan.","DOI":"10.1109\/WAINA.2017.116"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Manchanda, P., Singh, R., and Aggarwal, S. (2018, January 15\u201320). A computational approach to feature extraction for identification of suicidal ideation in tweets. Proceedings of the ACL 2018, Student Research Workshop, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-3013"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Alada\u011f, A.E., Muderrisoglu, S., Akbas, N.B., Zahmacioglu, O., and Bingol, H.O. (2018). Detecting suicidal ideation on forums: Proof-of-Concept study. J. Med. Internet Res., 20.","DOI":"10.2196\/preprints.9840"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C., Jiang, F., and Yang, H. (2017, January 13\u201317). A hybrid framework for text modeling with convolutional rnn. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098140"},{"key":"ref_30","unstructured":"Sawhney, R., Manchanda, P., Mathur, P., Shah, R., and Singh, R. (November, January 31). Exploring and learning suicidal ideation connotations on social media with deep learning. Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ji, S., Yu, C.P., Fung, S.f., Pan, S., and Long, G. (2018). Supervised learning for suicidal ideation detection in online user content. Complexity, 2018.","DOI":"10.1155\/2018\/6157249"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kalchbrenner, N., Grefenstette, E., and Blunsom, P. (2014). A convolutional neural network for modelling sentences. arXiv.","DOI":"10.3115\/v1\/P14-1062"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yin, W., and Sch\u00fctze, H. (2016). Multichannel variable-size convolution for sentence classification. arXiv.","DOI":"10.18653\/v1\/K15-1021"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E.T., Wu, J.T., Welt, J., Foote, J., Moseley, E.T., Grant, D.W., and Tyler, P.D. (2018). Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0192360"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Morales, M., Dey, P., Theisen, T., Belitz, D., and Chernova, N. (2019, January 6). An investigation of deep learning systems for suicide risk assessment. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/W19-3023"},{"key":"ref_36","unstructured":"Bhat, H.S., and Goldman-Mellor, S.J. (2017). Predicting Adolescent Suicide Attempts with Neural Networks. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gaur, M., Alambo, A., Sain, J.P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., Sheth, A., Welton, R., and Pathak, J. (2019, January 13\u201317). Knowledge-aware assessment of severity of suicide risk for early intervention. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313698"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Du, J., Zhang, Y., Luo, J., Jia, Y., Wei, Q., Tao, C., and Xu, H. (2018). Extracting psychiatric stressors for suicide from social media using deep learning. BMC Med. Inform. Decis. Mak., 18.","DOI":"10.1186\/s12911-018-0632-8"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yao, H., and Rosenthal, R.W.F. (2019). Detection of Suicidality among Opioid Users on Reddit: A Machine Learning Based Approach. J. Med. Internet Res.","DOI":"10.2196\/preprints.15293"},{"key":"ref_40","unstructured":"Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. (2015, January 7\u201312). Teaching machines to read and comprehend. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_41","unstructured":"Hill, F., Bordes, A., Chopra, S., and Weston, J. (2015). The goldilocks principle: Reading children\u2019s books with explicit memory representations. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, H., and Lin, J. (2016, January 12\u201317). Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1108"},{"key":"ref_43","unstructured":"Maziarz, M., Piasecki, M., Rudnicka, E., Szpakowicz, S., and K\u0119dzia, P. (2016, January 11\u201316). plWordNet 3.0\u2014A Comprehensive Lexical-Semantic Resource. Proceedings of the COLING 2016, the 26th International Conference on Computational Linguistics, Osaka, Japan."},{"key":"ref_44","unstructured":"Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O\u2019Reilly Media, Inc."},{"key":"ref_45","unstructured":"Sosa, P.M. (2019, December 23). Twitter Sentiment Analysis Using Combined LSTM-CNN Models. Available online: https:\/\/www.academia.edu\/35947062\/Twitter\/_Sentiment\/_Analysis\/_using\/_combined\/_LSTM-CNN\/_Models."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, J., Li, Y., Tian, J., and Li, T. (2018, January 12\u201314). LSTM-CNN Hybrid Model for Text Classification. Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2018.8577620"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s13673-019-0185-6","article-title":"Detection and classification of social media-based extremist affiliations using sentiment analysis techniques","volume":"9","author":"Ahmad","year":"2019","journal-title":"Hum.-Centric Comput. Inf. Sci."},{"key":"ref_48","unstructured":"Orabi, A.H., Buddhitha, P., Orabi, M.H., and Inkpen, D. (2018, January 5). Deep learning for depression detection of twitter users. Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, New Orleans, LA, USA."},{"key":"ref_49","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","unstructured":"Yan, S. (2019, July 16). Understanding LSTM Networks. Available online: https:\/\/colah.github.io\/posts\/2015-08-Understanding-LSTMs\/."},{"key":"ref_51","unstructured":"Olah, C., and Yan, S. (2019, July 16). Understanding LSTM and Its Diagrams. MLReview.com 2016. Available online: https:\/\/medium.com\/mlreview\/understanding-lstm-and-its-diagrams-37e2f46f1714."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"36","DOI":"10.3389\/frobt.2015.00036","article-title":"A taxonomy of deep convolutional neural nets for computer vision","volume":"2","author":"Srinivas","year":"2016","journal-title":"Front. Robot. AI"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_55","unstructured":"Zhang, X., Zhao, J., and LeCun, Y. (2015, January 7\u201312). Character-level convolutional networks for text classification. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_56","unstructured":"Sosa, P.M., and Sadigh, S. (2019, December 23). Twitter Sentiment Analysis with Neural Networks. Available online: https:\/\/www.academia.edu\/30498927\/Twitter_Sentiment_Analysis_with_Neural_Networks."},{"key":"ref_57","unstructured":"Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1142\/S0218488598000094","article-title":"The vanishing gradient problem during learning recurrent neural nets and problem solutions","volume":"6","author":"Hochreiter","year":"1998","journal-title":"Int. J. Uncertain. Fuzziness -Knowl.-Based Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Norouzi, M., Ranjbar, M., and Mori, G. (2009, January 20\u201325). Stacks of convolutional restricted boltzmann machines for shift-invariant feature learning. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206577"},{"key":"ref_60","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Nguyen, D., and Widrow, B. (1990, January 17\u201321). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. Proceedings of the 1990 IJCNN IEEE International Joint Conference on Neural Networks, San Diego, CA, USA.","DOI":"10.1109\/IJCNN.1990.137819"},{"key":"ref_62","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_63","unstructured":"Garcia, B., and Viesca, S.A. (2016). Real-time american sign language recognition with convolutional neural networks. Convolutional Neural Networks for Visual Recognition, Stanford University."},{"key":"ref_64","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Manning, C.D., and Raghavan, P. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Lecture Notes in Computer Science, Proceedings of the European Conference on Machine Learning, Chemnitz, Germany, 21\u201323 April 1998, Springer.","DOI":"10.1007\/BFb0026683"},{"key":"ref_67","unstructured":"De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013, January 8\u201311). Predicting depression via social media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Bosten, MA, USA."},{"key":"ref_68","unstructured":"Rish, I. (2001, January 4\u20136). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, New York, NY, USA."},{"key":"ref_69","unstructured":"McCallum, A., and Nigam, K. (1998, January 26\u201327). A comparison of event models for naive bayes text classification. Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, USA."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/505282.505283","article-title":"Machine learning in automated text categorization","volume":"34","author":"Sebastiani","year":"2002","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_71","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J.-Jpn. Soc. Artif. Intell."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Schapire, R.E., Singer, Y., and Singhal, A. (1998, January 24\u201328). Boosting and Rocchio applied to text filtering. Proceedings of the SIGIR, Melbourne, Australia.","DOI":"10.1145\/290941.290996"},{"key":"ref_73","unstructured":"Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_76","first-page":"966","article-title":"Text classification using machine learning techniques","volume":"4","author":"Ikonomakis","year":"2005","journal-title":"WSEAS Trans. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Qian, X. (2008, January 12\u201314). Text categorization based on LDA and SVM. Proceedings of the 2008 IEEE International Conference on Computer Science and Software Engineering, Wuhan, China.","DOI":"10.1109\/CSSE.2008.571"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Fiori, A. (2014). Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding: Revolutionizing Knowledge Understanding, IGI Global.","DOI":"10.4018\/978-1-4666-5019-0"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Basu, T., and Murthy, C. (2012, January 15\u201318). A feature selection method for improved document classification. Proceedings of the International Conference on Advanced Data Mining and Applications, Nanjing, China.","DOI":"10.1007\/978-3-642-35527-1_25"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1097\/00006842-200107000-00001","article-title":"Word use in the poetry of suicidal and nonsuicidal poets","volume":"63","author":"Stirman","year":"2001","journal-title":"Psychosom. Med."},{"key":"ref_82","unstructured":"Zhang, Y., and Wallace, B. (2015). A sensitivity analysis of (and practitioners\u2019 guide to) convolutional neural networks for sentence classification. arXiv."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Ranzato, M., Monga, R., Mao, M., Yang, K., Le, Q.V., Nguyen, P., Senior, A., Vanhoucke, V., and Dean, J. (2013, January 26\u201331). On rectified linear units for speech processing. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638312"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Dahl, G.E., Sainath, T.N., and Hinton, G.E. (2013, January 26\u201331). Improving deep neural networks for LVCSR using rectified linear units and dropout. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639346"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Vu, N.T., Adel, H., Gupta, P., and Sch\u00fctze, H. (2016). Combining recurrent and convolutional neural networks for relation classification. arXiv.","DOI":"10.18653\/v1\/N16-1065"},{"key":"ref_86","unstructured":"Ji, S., Pan, S., Li, X., Cambria, E., Long, G., and Huang, Z. (2019). Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/1\/7\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:24Z","timestamp":1760190324000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/13\/1\/7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"references-count":86,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["a13010007"],"URL":"https:\/\/doi.org\/10.3390\/a13010007","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]}}}