{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T11:54:49Z","timestamp":1773057289457,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030835262","type":"print"},{"value":"9783030835279","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-83527-9_5","type":"book-chapter","created":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T23:04:59Z","timestamp":1630278299000},"page":"60-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep Bag-of-Sub-Emotions for Depression Detection in Social Media"],"prefix":"10.1007","author":[{"given":"Juan S.","family":"Lara","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mario Ezra","family":"Arag\u00f3n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio A.","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Montes-y-G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,30]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Arag\u00f3n, M.E., L\u00f3pez-Monroy, A.P., Gonz\u00e1lez-Gurrola, L.C., G\u00f3mez, M.M.: Detecting depression in social media using fine-grained emotions. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019)","DOI":"10.18653\/v1\/N19-1151"},{"key":"5_CR2","unstructured":"Bromet, R.K.E., Jonge, P., Shahly, V., Wilcox, M.: The burden of depressive illness. In: Public Health Perspectives on Depressive Disorders (2017)"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Cong, Q., Feng, Z., Li, F., Xiang, Y., Rao, G., Tao, C.: XA-BiLSTM: a deep learning approach for depression detection in imbalanced data. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1624\u20131627. IEEE (2018)","DOI":"10.1109\/BIBM.2018.8621230"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Coppersmith, G., Ngo, K., Leary, R., Wood, A.: Exploratory analysis of social media prior to a suicide attempt. In: Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, pp. 106\u2013117 (2016)","DOI":"10.18653\/v1\/W16-0311"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"De Choudhury, M., Counts, S., Horvitz, E.J., Hoff, A.: Characterizing and predicting postpartum depression from shared Facebook data. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 626\u2013638 (2014)","DOI":"10.1145\/2531602.2531675"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., Kumar, M.: Discovering shifts to suicidal ideation from mental health content in social media. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2098\u20132110 (2016)","DOI":"10.1145\/2858036.2858207"},{"issue":"44","key":"5_CR7","doi-asserted-by":"publisher","first-page":"11203","DOI":"10.1073\/pnas.1802331115","volume":"115","author":"JC Eichstaedt","year":"2018","unstructured":"Eichstaedt, J.C., et al.: Facebook language predicts depression in medical records. Proc. Nat. Acad. Sci. 115(44), 11203\u201311208 (2018)","journal-title":"Proc. Nat. Acad. Sci."},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"45141","DOI":"10.1038\/srep45141","volume":"7","author":"G Gkotsis","year":"2017","unstructured":"Gkotsis, G., et al.: Characterisation of mental health conditions in social media using informed deep learning. Sci. Rep. 7, 45141 (2017)","journal-title":"Sci. Rep."},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.cobeha.2017.07.005","volume":"18","author":"SC Guntuku","year":"2017","unstructured":"Guntuku, S.C., Yaden, D.B., Kern, M.L., Ungar, L.H., Eichstaedt, J.C.: Detecting depression and mental illness on social media: an integrative review. Curr. Opinion Behav. Sci. 18, 43\u201349 (2017)","journal-title":"Curr. Opinion Behav. Sci."},{"key":"5_CR10","unstructured":"Lara, J.S., Gonz\u00e1lez, F.A.: Dissimilarity mixture autoencoder for deep clustering. arXiv:2006.08177 (2020)"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, X., Hovy, E.H., Jurafsky, D.: Visualizing and understanding neural models in NLP. In: HLT-NAACL (2016)","DOI":"10.18653\/v1\/N16-1082"},{"key":"5_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-319-65813-1_30","volume-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction","author":"DE Losada","year":"2017","unstructured":"Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 346\u2013360. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-65813-1_30"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk 2018: early risk prediction on the internet (extended lab overview). In: Proceedings of the 9th International Conference of the CLEF Association. CLEF 2018, Avignon, France (2018)","DOI":"10.1007\/978-3-319-98932-7_30"},{"issue":"3","key":"5_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","volume":"29","author":"SM Mohammad","year":"2012","unstructured":"Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436\u2013465 (2012)","journal-title":"Comput. Intell."},{"key":"5_CR15","unstructured":"Orabi, A.H., Buddhitha, P., Orabi, M.H., Inkpen, D.: Deep learning for depression detection of twitter users. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 88\u201397 (2018)"},{"issue":"1","key":"5_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-12961-9","volume":"7","author":"AG Reece","year":"2017","unstructured":"Reece, A.G., Reagan, A.J., Lix, K.L., Dodds, P.S., Danforth, C.M., Langer, E.J.: Forecasting the onset and course of mental illness with twitter data. Sci. Rep. 7(1), 1\u201311 (2017)","journal-title":"Sci. Rep."},{"key":"5_CR17","unstructured":"Renteria-Rodriguez, M.E.: Salud mental en mexico. NOTA-INCyTU N\u00daMERO 007 (2018)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Manchanda, P., Singh, R., Aggarwal, S.: A computational approach to feature extraction for identification of suicidal ideation in tweets. In: Proceedings of ACL 2018, Student Research Workshop, pp. 91\u201398 (2018)","DOI":"10.18653\/v1\/P18-3013"},{"issue":"1","key":"5_CR19","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1177\/0261927X09351676","volume":"29","author":"YR Tausczik","year":"2010","unstructured":"Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24\u201354 (2010)","journal-title":"J. Lang. Soc. Psychol."},{"key":"5_CR20","unstructured":"Thavikulwat, P.: Affinity propagation: a clustering algorithm for computer-assisted business simulation and experimental exercises. In: Developments in Business Simulation and Experiential Learning (2008)"},{"key":"5_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1007\/978-3-319-06269-3_10","volume-title":"Health Information Science","author":"Y Xue","year":"2014","unstructured":"Xue, Y., Li, Q., Jin, L., Feng, L., Clifton, D.A., Clifford, G.D.: Detecting adolescent psychological pressures from micro-blog. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds.) HIS 2014. LNCS, vol. 8423, pp. 83\u201394. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-06269-3_10"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Yazdavar, A.H., et al.: Semi-supervised approach to monitoring clinical depressive symptoms in social media. In: Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, pp. 1191\u20131198 (2017)","DOI":"10.1145\/3110025.3123028"}],"container-title":["Lecture Notes in Computer Science","Text, Speech, and Dialogue"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-83527-9_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,29]],"date-time":"2021-08-29T23:06:55Z","timestamp":1630278415000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-83527-9_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030835262","9783030835279"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-83527-9_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TSD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Text, Speech, and Dialogue","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Olomouc","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tsd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.kiv.zcu.cz\/tsd2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"TSDEngine 3.2","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"101","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,93","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}