{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T16:06:55Z","timestamp":1750694815676,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031490170"},{"type":"electronic","value":"9783031490187"}],"license":[{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,27]],"date-time":"2023-11-27T00:00:00Z","timestamp":1701043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-49018-7_24","type":"book-chapter","created":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:02:21Z","timestamp":1701039741000},"page":"327-342","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Depression Detection Using Deep Learning and\u00a0Natural Language Processing Techniques: A Comparative Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7000-6967","authenticated-orcid":false,"given":"Francisco","family":"Mesquita","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8234-9481","authenticated-orcid":false,"given":"Jos\u00e9","family":"Maur\u00edcio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5834-6571","authenticated-orcid":false,"given":"Gon\u00e7alo","family":"Marques","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,27]]},"reference":[{"key":"24_CR1","unstructured":"Depression. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/depression. Accessed 26 Oct 2022"},{"key":"24_CR2","unstructured":"TF-IDF for Document Ranking from scratch in python on real world dataset. https:\/\/towardsdatascience.com\/tf-idf-for-document-ranking-from-scratch-in-python-on-real-world-dataset-796d339a4089. Accessed 09 Jan 2023"},{"key":"24_CR3","doi-asserted-by":"crossref","unstructured":"Al-Garaady, J., Mahyoob, M.: Public sentiment analysis in social media on the SARS-CoV-2 vaccination using VADER lexicon polarity (2022)","DOI":"10.31235\/osf.io\/nk2j6"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Almeida, F., Xex\u00e9o, G.: Word embeddings: a survey (2019). https:\/\/doi.org\/10.48550\/arXiv.1901.09069","DOI":"10.48550\/arXiv.1901.09069"},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Alsagri, H.S., Ykhlef, M.: Machine learning-based approach for depression detection in twitter using content and activity features. IEICE Trans. Inf. Syst. E103.D(8), 1825\u20131832 (2020). https:\/\/doi.org\/10.1587\/transinf.2020EDP7023","DOI":"10.1587\/transinf.2020EDP7023"},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s42979-021-00958-1","volume":"3","author":"NV Babu","year":"2021","unstructured":"Babu, N.V., Kanaga, E.G.M.: Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput. Sci. 3(1), 74 (2021). https:\/\/doi.org\/10.1007\/s42979-021-00958-1","journal-title":"SN Comput. Sci."},{"issue":"11","key":"24_CR7","first-page":"5411","volume":"12","author":"C Bhargava","year":"2021","unstructured":"Bhargava, C., Al, E.: Depression detection using sentiment analysis of tweets. Turk. J. Comput. Math. Educ. (TURCOMAT) 12(11), 5411\u20135418 (2021)","journal-title":"Turk. J. Comput. Math. Educ. (TURCOMAT)"},{"key":"24_CR8","unstructured":"Biswas, S., Ghosh, S.: Drug usage analysis by VADER sentiment analysis on leading countries. Mapana J. Sci. 21(3) (2022)"},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Dessai, S., Usgaonkar, S.S.: Depression detection on social media using text mining. In: 2022 3rd International Conference for Emerging Technology (INCET), pp. 1\u20134 (2022). https:\/\/doi.org\/10.1109\/INCET54531.2022.9824931","DOI":"10.1109\/INCET54531.2022.9824931"},{"key":"24_CR10","doi-asserted-by":"publisher","unstructured":"Elbagir, S., Yang, J.: Sentiment analysis on twitter with Python\u2019s natural language toolkit and VADER sentiment analyzer. In: IAENG Transactions on Engineering Sciences, pp. 63\u201380. WORLD SCIENTIFIC (2019). https:\/\/doi.org\/10.1142\/9789811215094_0005","DOI":"10.1142\/9789811215094_0005"},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"29","DOI":"10.5120\/ijca2017914022","volume":"165","author":"B Gupta","year":"2017","unstructured":"Gupta, B., Negi, M., Vishwakarma, K., Rawat, G., Badhani, P.: Study of twitter sentiment analysis using machine learning algorithms on Python. Int. J. Comput. Appl. 165, 29\u201334 (2017). https:\/\/doi.org\/10.5120\/ijca2017914022","journal-title":"Int. J. Comput. Appl."},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Hossain, M.S., Rahman, M.F.: Customer sentiment analysis and prediction of insurance products\u2019 reviews using machine learning approaches. FIIB Bus. Rev. (2022). https:\/\/doi.org\/10.1177\/23197145221115793","DOI":"10.1177\/23197145221115793"},{"key":"24_CR13","doi-asserted-by":"publisher","unstructured":"Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, no. 1, pp. 216\u2013225 (2014). https:\/\/doi.org\/10.1609\/icwsm.v8i1.14550","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"107503","DOI":"10.1016\/j.chb.2022.107503","volume":"139","author":"M Kabir","year":"2023","unstructured":"Kabir, M., et al.: DEPTWEET: a typology for social media texts to detect depression severities. Comput. Hum. Behav. 139, 107503 (2023). https:\/\/doi.org\/10.1016\/j.chb.2022.107503","journal-title":"Comput. Hum. Behav."},{"key":"24_CR15","doi-asserted-by":"publisher","unstructured":"Kolchyna, O., Souza, T.T.P., Treleaven, P., Aste, T.: Twitter sentiment analysis: lexicon method, machine learning method and their combination (2015). https:\/\/doi.org\/10.48550\/arXiv.1507.00955","DOI":"10.48550\/arXiv.1507.00955"},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Arias-de La Torre, J., et al.: Prevalence and variability of current depressive disorder in 27 European countries: a population-based study. Lancet Publ. Health 6(10), e729\u2013e738 (2021). https:\/\/doi.org\/10.1016\/S2468-2667(21)00047-5","DOI":"10.1016\/S2468-2667(21)00047-5"},{"issue":"10","key":"24_CR17","doi-asserted-by":"publisher","first-page":"484","DOI":"10.3390\/info13100484","volume":"13","author":"JJE Macrohon","year":"2022","unstructured":"Macrohon, J.J.E., Villavicencio, C.N., Inbaraj, X.A., Jeng, J.H.: A semi-supervised approach to sentiment analysis of tweets during the 2022 Philippine presidential election. Information 13(10), 484 (2022). https:\/\/doi.org\/10.3390\/info13100484","journal-title":"Information"},{"issue":"5","key":"24_CR18","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1007\/s10796-021-10107-x","volume":"23","author":"S Mendon","year":"2021","unstructured":"Mendon, S., Dutta, P., Behl, A., Lessmann, S.: A hybrid approach of machine learning and lexicons to sentiment analysis: enhanced insights from twitter data of natural disasters. Inf. Syst. Front. 23(5), 1145\u20131168 (2021). https:\/\/doi.org\/10.1007\/s10796-021-10107-x","journal-title":"Inf. Syst. Front."},{"issue":"1","key":"24_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","volume":"2","author":"MM Najafabadi","year":"2015","unstructured":"Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015). https:\/\/doi.org\/10.1186\/s40537-014-0007-7","journal-title":"J. Big Data"},{"key":"24_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-319-93846-2_45","volume-title":"Artificial Intelligence in Education","author":"H Newman","year":"2018","unstructured":"Newman, H., Joyner, D.: Sentiment analysis of student evaluations of teaching. In: Penstein Ros\u00e9, C., Mart\u00ednez-Maldonado, R., Hoppe, H.U., Luckin, R., Mavrikis, M., Porayska-Pomsta, K., McLaren, B., du Boulay, B. (eds.) AIED 2018. LNCS (LNAI), vol. 10948, pp. 246\u2013250. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93846-2_45"},{"key":"24_CR21","first-page":"89","volume":"3","author":"TN Prakash","year":"2019","unstructured":"Prakash, T.N., Aloysius, A.: Data preprocessing in sentiment analysis using twitter data. Int. Educ. Appl. Res. J. 3, 89\u201392 (2019)","journal-title":"Int. Educ. Appl. Res. J."},{"key":"24_CR22","doi-asserted-by":"publisher","unstructured":"Ramadhani, A.M., Goo, H.S.: Twitter sentiment analysis using deep learning methods. In: 2017 7th International Annual Engineering Seminar (InAES), pp. 1\u20134 (2017). https:\/\/doi.org\/10.1109\/INAES.2017.8068556","DOI":"10.1109\/INAES.2017.8068556"},{"issue":"12","key":"24_CR23","doi-asserted-by":"publisher","first-page":"e11817","DOI":"10.2196\/11817","volume":"20","author":"BJ Ricard","year":"2018","unstructured":"Ricard, B.J., Marsch, L.A., Crosier, B., Hassanpour, S.: Exploring the utility of community-generated social media content for detecting depression: an analytical study on Instagram. J. Med. Internet Res. 20(12), e11817 (2018). https:\/\/doi.org\/10.2196\/11817","journal-title":"J. Med. Internet Res."},{"key":"24_CR24","doi-asserted-by":"publisher","unstructured":"Shailaja, K., Seetharamulu, B., Jabbar, M.A.: Machine learning in healthcare: a review. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 910\u2013914. IEEE (2018). https:\/\/doi.org\/10.1109\/ICECA.2018.8474918","DOI":"10.1109\/ICECA.2018.8474918"},{"issue":"1","key":"24_CR25","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s12874-019-0681-4","volume":"19","author":"JAM Sidey-Gibbons","year":"2019","unstructured":"Sidey-Gibbons, J.A.M., Sidey-Gibbons, C.J.: Machine learning in medicine: a practical introduction. BMC Med. Res. Methodol. 19(1), 64 (2019). https:\/\/doi.org\/10.1186\/s12874-019-0681-4","journal-title":"BMC Med. Res. Methodol."},{"issue":"S6","key":"24_CR26","doi-asserted-by":"publisher","first-page":"S28","DOI":"10.5694\/mja12.10628","volume":"199","author":"JWG Tiller","year":"2013","unstructured":"Tiller, J.W.G.: Depression and anxiety. Med. J. Aust. 199(S6), S28\u2013S31 (2013). https:\/\/doi.org\/10.5694\/mja12.10628","journal-title":"Med. J. Aust."},{"key":"24_CR27","unstructured":"tweets, Hemanthkumar, Latha: Depression detection with sentiment analysis of tweets. Turk. J. Comput. Math. Educ. (2019)"},{"key":"24_CR28","doi-asserted-by":"publisher","unstructured":"Wani, M.A., ELAffendi, M.A., Shakil, K.A., Imran, A.S., El-Latif, A.A.A.: Depression screening in humans with AI and deep learning techniques. IEEE Trans. Comput. Soc. Syst. (2022). https:\/\/doi.org\/10.1109\/TCSS.2022.3200213","DOI":"10.1109\/TCSS.2022.3200213"},{"issue":"1","key":"24_CR29","first-page":"19","volume":"7","author":"C Woods","year":"2021","unstructured":"Woods, C., Adedeji, M.: Classification of depression through social media posts using machine learning techniques. Univ. Ibadan J. Sci. Logics ICT Res. 7(1), 19\u201328 (2021)","journal-title":"Univ. Ibadan J. Sci. Logics ICT Res."},{"key":"24_CR30","doi-asserted-by":"publisher","unstructured":"Yadav, N., Kudale, O., Rao, A., Gupta, S., Shitole, A.: Twitter sentiment analysis using supervised machine learning. In: Hemanth, J., Bestak, R., Chen, J.I.Z. (eds.) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, pp. 631\u2013642. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-981-15-9509-7_51","DOI":"10.1007\/978-981-15-9509-7_51"},{"key":"24_CR31","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.jad.2019.01.026","volume":"248","author":"S Yoon","year":"2019","unstructured":"Yoon, S., Kleinman, M., Mertz, J., Brannick, M.: Is social network site usage related to depression? A meta-analysis of Facebook-depression relations. J. Affect. Disord. 248, 65\u201372 (2019). https:\/\/doi.org\/10.1016\/j.jad.2019.01.026","journal-title":"J. Affect. Disord."},{"key":"24_CR32","doi-asserted-by":"publisher","unstructured":"Zhou, B., Yang, G., Shi, Z., Ma, S.: Natural language processing for smart healthcare. IEEE Rev. Biomed. Eng., 1\u201317 (2022). https:\/\/doi.org\/10.1109\/RBME.2022.3210270","DOI":"10.1109\/RBME.2022.3210270"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-49018-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T23:11:25Z","timestamp":1701040285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-49018-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,27]]},"ISBN":["9783031490170","9783031490187"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-49018-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Coimbra","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2023","order":10,"name":"conference_id","label":"Conference ID","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":"Conftool","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","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":"61","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":"0","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":"58% - 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":"2","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":"5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}