{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:51:03Z","timestamp":1778169063751,"version":"3.51.4"},"reference-count":109,"publisher":"Springer Science and Business Media LLC","issue":"S3","license":[{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"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":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10462-023-10603-3","type":"journal-article","created":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T09:02:07Z","timestamp":1695459727000},"page":"3273-3297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Emotion and personality analysis and detection using natural language processing, advances, challenges and future scope"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6107-559X","authenticated-orcid":false,"given":"Faezeh","family":"Safari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7217-905X","authenticated-orcid":false,"given":"Abdolah","family":"Chalechale","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"issue":"8","key":"10603_CR1","doi-asserted-by":"crossref","first-page":"5789","DOI":"10.1007\/s10462-021-09958-2","volume":"54","author":"FA Acheampong","year":"2021","unstructured":"Acheampong FA, Nunoo-Mensah H, Chen W (2021) Transformer models for text-based emotion detection: a review of bert-based approaches. Artificial Intell Rev 54(8):5789\u20135829","journal-title":"Artificial Intell Rev"},{"key":"10603_CR2","doi-asserted-by":"crossref","first-page":"146214","DOI":"10.1109\/ACCESS.2021.3121791","volume":"9","author":"H Ahmad","year":"2021","unstructured":"Ahmad H, Asghar MU, Asghar MZ, Khan A, Mosavi AH (2021) A hybrid deep learning technique for personality trait classification from text. IEEE Access 9:146214\u2013146232","journal-title":"IEEE Access"},{"key":"10603_CR3","doi-asserted-by":"crossref","unstructured":"Al-Omari H, Abdullah MA, Shaikh S (2020) Emodet2: Emotion detection in english textual dialogue using bert and bilstm models. In: 2020 11th Int Conf Inform Commun Syst (ICICS), pp 226\u2013232. IEEE","DOI":"10.1109\/ICICS49469.2020.239539"},{"key":"10603_CR4","doi-asserted-by":"crossref","unstructured":"Alla KR, Kandibanda N, Katta P, Muthavarapu A, Kuchibhotla S (2022) Emotion detection from text using lstm. In: Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, London, Volume 3, pp 545\u2013553. Springer","DOI":"10.1007\/978-981-16-1781-2_49"},{"key":"10603_CR5","doi-asserted-by":"crossref","unstructured":"Almanie T, Aldayel A, Alkanhal G, Alesmail L, Almutlaq M, Althunayan R (2018) Saudi mood: a real-time informative tool for visualizing emotions in saudi arabia using twitter. In: 2018 21st Saudi computer society national computer conference (NCC), pp 1\u20136. IEEE","DOI":"10.1109\/NCG.2018.8593165"},{"key":"10603_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.simpa.2021.100179","volume":"10","author":"N Alvarez-Gonzalez","year":"2021","unstructured":"Alvarez-Gonzalez N, Kaltenbrunner A, G\u00f3mez V (2021) Emotion-core: an open source framework for emotion detection research. Softw Impacts 10:100179","journal-title":"Softw Impacts"},{"key":"10603_CR7","doi-asserted-by":"crossref","unstructured":"Alvarez-Gonzalez N, Kaltenbrunner A, G\u00f3mez V (2021) Uncovering the limits of text-based emotion detection. arXiv preprintarXiv:2109.01900","DOI":"10.18653\/v1\/2021.findings-emnlp.219"},{"key":"10603_CR8","doi-asserted-by":"crossref","first-page":"19512","DOI":"10.1109\/ACCESS.2023.3248506","volume":"11","author":"F Anzum","year":"2023","unstructured":"Anzum F, Gavrilova ML (2023) Emotion detection from micro-blogs using novel input representation. IEEE Access 11:19512\u201319522","journal-title":"IEEE Access"},{"issue":"3","key":"10603_CR9","doi-asserted-by":"crossref","first-page":"310","DOI":"10.5391\/IJFIS.2021.21.3.310","volume":"21","author":"JE Arijanto","year":"2021","unstructured":"Arijanto JE, Geraldy S, Tania C, Suhartono D (2021) Personality prediction based on text analytics using bidirectional encoder representations from transformers from english twitter dataset. Int J Fuzzy Logic Intell Syst 21(3):310\u2013316","journal-title":"Int J Fuzzy Logic Intell Syst"},{"key":"10603_CR10","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1162\/tacl_a_00288","volume":"7","author":"M Artetxe","year":"2019","unstructured":"Artetxe M, Schwenk H (2019) Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Trans Assoc Comput Linguistics 7:597\u2013610","journal-title":"Trans Assoc Comput Linguistics"},{"key":"10603_CR11","doi-asserted-by":"crossref","unstructured":"Barbieri F, Camacho-Collados J, Espinosa\u00a0Anke L, Neves L (2020) TweetEval: Unified benchmark and comparative evaluation for tweet classification. pp 1644\u20131650","DOI":"10.18653\/v1\/2020.findings-emnlp.148"},{"issue":"5","key":"10603_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528576","volume":"22","author":"MF Bashir","year":"2023","unstructured":"Bashir MF, Javed AR, Arshad MU, Gadekallu TR, Shahzad W, Beg MO (2023) Context-aware emotion detection from low-resource urdu language using deep neural network. ACM Trans Asian Low-Resource Lang Inform Process 22(5):1\u201330","journal-title":"ACM Trans Asian Low-Resource Lang Inform Process"},{"key":"10603_CR13","doi-asserted-by":"crossref","unstructured":"Bharadwaj S, Sridhar S, Choudhary R, Srinath R (2018) Persona traits identification based on myers-briggs type indicator (mbti)-a text classification approach. In: 2018 international conference on advances in computing, communications and informatics (ICACCI), pp 1076\u20131082. IEEE","DOI":"10.1109\/ICACCI.2018.8554828"},{"issue":"1","key":"10603_CR14","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/TMM.2012.2225032","volume":"15","author":"J-I Biel","year":"2012","unstructured":"Biel J-I, Gatica-Perez D (2012) The youtube lens: crowdsourced personality impressions and audiovisual analysis of vlogs. IEEE Trans Multimedia 15(1):41\u201355","journal-title":"IEEE Trans Multimedia"},{"key":"10603_CR15","doi-asserted-by":"crossref","unstructured":"Biel J-I, Tsiminaki V, Dines J, Gatica-Perez D (2013) Hi youtube! personality impressions and verbal content in social video. In: Proceedings of the 15th ACM on International conference on multimodal interaction, pp 119\u2013126","DOI":"10.1145\/2522848.2522877"},{"key":"10603_CR16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski P, Grave E, Joulin A, Mikolov T (2017) Enriching word vectors with subword information. Trans Associ Comput Linguistics 5:135\u2013146","journal-title":"Trans Associ Comput Linguistics"},{"issue":"92","key":"10603_CR17","first-page":"1","volume":"20","author":"F Bravo-Marquez","year":"2019","unstructured":"Bravo-Marquez F, Frank E, Pfahringer B, Mohammad SM (2019) Affectivetweets: a weka package for analyzing affect in tweets. J Mach Learn Res 20(92):1\u20136","journal-title":"J Mach Learn Res"},{"key":"10603_CR18","doi-asserted-by":"crossref","unstructured":"Buechel S, Hahn U (2017) Readers vs. writers vs. texts: Coping with different perspectives of text understanding in emotion annotation. In: Proceedings of the 11th Linguistic Annotation Workshop, pp 1\u201312","DOI":"10.18653\/v1\/W17-0801"},{"key":"10603_CR19","doi-asserted-by":"crossref","unstructured":"Cahyani DE, Faishal AF (2020) Classification of big five personality behavior tendencies based on study field with twitter analysis using support vector machine. In: 2020 7th International conference on information technology, computer, and electrical engineering (ICITACEE), pp 140\u2013145. IEEE","DOI":"10.1109\/ICITACEE50144.2020.9239130"},{"key":"10603_CR20","doi-asserted-by":"crossref","unstructured":"Celli F, Lepri B (2018) Is big five better than mbti? a personality computing challenge using twitter data. Computational Linguistics CLiC-it 2018, p\u00a093","DOI":"10.4000\/books.aaccademia.3147"},{"key":"10603_CR21","doi-asserted-by":"crossref","unstructured":"Cer D, Yang Y, Kong S-y, Hua N, Limtiaco N, St.\u00a0John R, Constant N, Guajardo-Cespedes M, Yuan S, Tar C, Strope B, Kurzweil R (2018) Universal sentence encoder for English. In: Proceedings of the 2018 conference on empirical methods in natural language processing: system demonstrations, pp 169\u2013174, Brussels, Belgium. Association for Computational Linguistics","DOI":"10.18653\/v1\/D18-2029"},{"key":"10603_CR22","doi-asserted-by":"crossref","unstructured":"Chen Y-H, Choi JD (2016) Character identification on multiparty conversation: Identifying mentions of characters in tv shows. In: Proceedings of the 17th Annual meeting of the special interest group on discourse and dialogue, pp 90\u2013100","DOI":"10.18653\/v1\/W16-3612"},{"key":"10603_CR23","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.11382","volume":"9","author":"EJ Choong","year":"2021","unstructured":"Choong EJ, Varathan KD (2021) Predicting judging-perceiving of myers-briggs type indicator (mbti) in online social forum. PeerJ 9:e11382","journal-title":"PeerJ"},{"key":"10603_CR24","doi-asserted-by":"crossref","unstructured":"Chowanda A, Sutoyo R, Meiliana, Tanachutiwat S (2021) Exploring text-based emotions recognition machine learning techniques on social media conversation. Procedia Computer Science, 179:821\u2013828. 5th International conference on computer science and computational intelligence 2020","DOI":"10.1016\/j.procs.2021.01.099"},{"key":"10603_CR25","doi-asserted-by":"crossref","unstructured":"Chowdhary K (2020) Natural language processing. Fundamentals of artificial intelligence, pp 603\u2013649","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"10603_CR26","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511596544","volume-title":"The Cambridge handbook of personality psychology. Cambridge handbooks in psychology","author":"P Corr","year":"2009","unstructured":"Corr P, Matthews G (2009) The Cambridge handbook of personality psychology. Cambridge handbooks in psychology. Cambridge University Press"},{"key":"10603_CR27","doi-asserted-by":"crossref","unstructured":"Demszky D, Movshovitz-Attias D, Ko J, Cowen A, Nemade G, Ravi S (2020) GoEmotions: a dataset of fine-grained emotions. pp 4040\u20134054","DOI":"10.18653\/v1\/2020.acl-main.372"},{"key":"10603_CR28","doi-asserted-by":"crossref","unstructured":"Dutta I, Athilakshmi R, Amulya (2023) Personality prediction using deep learning. 2023 third international conference on advances in electrical. computing, communication and sustainable technologies (ICAECT), pp 1\u20135","DOI":"10.1109\/ICAECT57570.2023.10117573"},{"issue":"1","key":"10603_CR29","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/00223980.1957.9713059","volume":"43","author":"P Ekman","year":"1957","unstructured":"Ekman P (1957) A methodological discussion of nonverbal behavior. J Psychol 43(1):141\u2013149","journal-title":"J Psychol"},{"issue":"3","key":"10603_CR30","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1037\/0033-295X.99.3.550","volume":"99","author":"P Ekman","year":"1992","unstructured":"Ekman P (1992) Are there basic emotions? Psychol Rev 99(3):550\u2013553","journal-title":"Psychol Rev"},{"issue":"1","key":"10603_CR31","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.eij.2021.05.004","volume":"23","author":"K El-Demerdash","year":"2022","unstructured":"El-Demerdash K, El-Khoribi RA, Shoman MAI, Abdou S (2022) Deep learning based fusion strategies for personality prediction. Egyp Inform J 23(1):47\u201353","journal-title":"Egyp Inform J"},{"key":"10603_CR32","doi-asserted-by":"crossref","unstructured":"Feng Y, Liu K (2021) A personality prediction method of WEIBO users based on personality lexicon. In: Natural Language Processing. Academy and Industry Research Collaboration Center (AIRCC)","DOI":"10.5121\/csit.2021.112312"},{"key":"10603_CR33","volume-title":"The emotions. Studies in emotion and social interaction","author":"N Frijda","year":"1986","unstructured":"Frijda N (1986) The emotions. Studies in emotion and social interaction. Cambridge University Press"},{"issue":"1","key":"10603_CR34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-99269-x","volume":"12","author":"S Ghosh","year":"2022","unstructured":"Ghosh S, Ekbal A, Bhattacharyya P (2022) Deep cascaded multitask framework for detection of temporal orientation, sentiment and emotion from suicide notes. Sci Rep 12(1):1\u201316","journal-title":"Sci Rep"},{"key":"10603_CR35","doi-asserted-by":"crossref","unstructured":"Gjurkovi\u0107 M, Karan M, Vukojevi\u0107 I, Bo\u0161njak M, Snajder J (2021) PANDORA talks: Personality and demographics on Reddit. In: Proceedings of the ninth international workshop on natural language processing for social media, pp 138\u2013152, Online. Association for Computational Linguistics","DOI":"10.18653\/v1\/2021.socialnlp-1.12"},{"key":"10603_CR36","doi-asserted-by":"crossref","unstructured":"Gupta S, Singh A, Ranjan J (2023) Multimodal, multiview and multitasking depression detection framework endorsed with auxiliary sentiment polarity and emotion detection. Int J Syst Assurance Eng Manag, pp 1\u201316","DOI":"10.1007\/s13198-023-01861-z"},{"key":"10603_CR37","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106443","volume":"208","author":"Z Halim","year":"2020","unstructured":"Halim Z, Waqar M, Tahir M (2020) A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email. Knowl-Based Syst 208:106443","journal-title":"Knowl-Based Syst"},{"key":"10603_CR38","doi-asserted-by":"crossref","first-page":"115345","DOI":"10.1109\/ACCESS.2020.3004002","volume":"8","author":"M Jayaratne","year":"2020","unstructured":"Jayaratne M, Jayatilleke B (2020) Predicting personality using answers to open-ended interview questions. IEEE Access 8:115345\u2013115355","journal-title":"IEEE Access"},{"key":"10603_CR39","unstructured":"John OP, Srivastava S (1999) The Big Five Trait taxonomy: History, measurement, and theoretical perspectives., pp 102\u2013138. Handbook of personality: Theory and research, 2nd ed. Guilford Press, New York, NY, US"},{"issue":"3","key":"10603_CR40","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1007\/s42979-023-01670-y","volume":"4","author":"SJ Johnson","year":"2023","unstructured":"Johnson SJ, Murty MR (2023) An aspect-aware enhanced psycholinguistic knowledge graph-based personality detection using deep learning. SN Comput Sci 4(3):293","journal-title":"SN Comput Sci"},{"key":"10603_CR41","volume-title":"Psychological types. Collected works of C.G. Jung","author":"CG Jung","year":"1976","unstructured":"Jung CG (1976) Psychological types. Collected works of C.G. Jung. Princeton University Press, Princeton, NJ"},{"key":"10603_CR42","doi-asserted-by":"crossref","unstructured":"Kaminska O, Cornelis C, Hoste V (2021) Nearest neighbour approaches for emotion detection in tweets. In: Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp 203\u2013212, Online. Association for Computational Linguistics","DOI":"10.1007\/978-3-030-87334-9_20"},{"key":"10603_CR43","doi-asserted-by":"crossref","unstructured":"Kaur P, Gosain A (2018) Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise. In: ICT Based Innovations, pp 23\u201330. Springer","DOI":"10.1007\/978-981-10-6602-3_3"},{"key":"10603_CR44","doi-asserted-by":"crossref","unstructured":"Kazemeini A, Roy SS, Mercer RE, Cambria E (2021) Interpretable representation learning for personality detection. In: 2021 International conference on data mining workshops (ICDMW), pp 158\u2013165. IEEE","DOI":"10.1109\/ICDMW53433.2021.00026"},{"key":"10603_CR45","doi-asserted-by":"crossref","unstructured":"Kerz E, Qiao Y, Zanwar S, Wiechmann D (2022) Pushing on personality detection from verbal behavior: a transformer meets text contours of psycholinguistic features. pp 182\u2013194","DOI":"10.18653\/v1\/2022.wassa-1.17"},{"issue":"1","key":"10603_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00387-6","volume":"8","author":"EAH Khalil","year":"2021","unstructured":"Khalil EAH, El Houby EM, Mohamed HK (2021) Deep learning for emotion analysis in arabic tweets. J Big Data 8(1):1\u201315","journal-title":"J Big Data"},{"key":"10603_CR47","doi-asserted-by":"crossref","unstructured":"Khan AS, Hussain A, Asghar MZ, Saddozai FK, Arif A, Khalid HA (2020) Personality classification from online text using machine learning approach. Int J Adv Comput Sci Appl 11(3)","DOI":"10.14569\/IJACSA.2020.0110358"},{"issue":"10","key":"10603_CR48","doi-asserted-by":"crossref","first-page":"8007","DOI":"10.1016\/j.aej.2022.01.050","volume":"61","author":"MA Kosan","year":"2022","unstructured":"Kosan MA, Karacan H, Urgen BA (2022) Predicting personality traits with semantic structures and lstm-based neural networks. Alexandria Eng J 61(10):8007\u20138025","journal-title":"Alexandria Eng J"},{"key":"10603_CR49","doi-asserted-by":"crossref","unstructured":"Kosan MA, Karacan H, Urgen BA (2023) Personality traits prediction model from turkish contents with semantic structures. Neural Comput Appl pp 1\u201319","DOI":"10.1007\/s00521-023-08603-z"},{"key":"10603_CR50","doi-asserted-by":"crossref","unstructured":"Krommyda M, Rigos A, Bouklas K, Amditis A (2020) Emotion detection in twitter posts: a rule-based algorithm for annotated data acquisition. In: 2020 international conference on computational science and computational intelligence (CSCI), pp 257\u2013262. IEEE","DOI":"10.1109\/CSCI51800.2020.00050"},{"issue":"5","key":"10603_CR51","first-page":"1","volume":"22","author":"A Kumar","year":"2023","unstructured":"Kumar A, Beniwal R, Jain D (2023) Personality detection using kernel-based ensemble model for leveraging social psychology in online networks. ACM Trans Asian Low-Resource Lang Inform Process 22(5):1\u201320","journal-title":"ACM Trans Asian Low-Resource Lang Inform Process"},{"key":"10603_CR52","unstructured":"Kumar S, Shrivatson Priyan RS, Padmavathy (2020) Personality prediction using twitter data. Int Res J Eng Technol (IRJET) 7(7):4878\u20134882"},{"key":"10603_CR53","doi-asserted-by":"crossref","unstructured":"Lee SJ, Lim J, Paas L, Ahn HS (2023) Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data. Neural Comput Appl pp 1\u201312","DOI":"10.1007\/s00521-023-08276-8"},{"key":"10603_CR54","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.neucom.2022.04.049","volume":"493","author":"Y Li","year":"2022","unstructured":"Li Y, Kazemeini A, Mehta Y, Cambria E (2022) Multitask learning for emotion and personality traits detection. Neurocomputing 493:340\u2013350","journal-title":"Neurocomputing"},{"key":"10603_CR55","unstructured":"Li Y, Su H, Shen X, Li W, Cao Z, Niu S (2017) DailyDialog: A manually labelled multi-turn dialogue dataset. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp 986\u2013995, Taipei, Taiwan. Asian Federation of Natural Language Processing"},{"key":"10603_CR56","doi-asserted-by":"crossref","unstructured":"Lu X, Zhao Y, Wu Y, Tian Y, Chen H, Qin B (2020) An iterative emotion interaction network for emotion recognition in conversations. In: proceedings of the 28th international conference on computational linguistics, pp 4078\u20134088","DOI":"10.18653\/v1\/2020.coling-main.360"},{"key":"10603_CR57","unstructured":"Luyckx K, Daelemans W (2008) Personae: a corpus for author and personality prediction from text. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC\u201908), Marrakech, Morocco. European Language Resources Association (ELRA)"},{"key":"10603_CR58","doi-asserted-by":"crossref","unstructured":"Lynam DR, Miller JD (2019) On the ubiquity and importance of antagonism. In: The Handbook of Antagonism, pp 1\u201324. Elsevier","DOI":"10.1016\/B978-0-12-814627-9.00001-3"},{"key":"10603_CR59","doi-asserted-by":"crossref","unstructured":"Maharani W, Effendy V (2022) Big five personality prediction based in indonesian tweets using machine learning methods. Int J Electr Comput Eng (2088-8708), 12(2)","DOI":"10.11591\/ijece.v12i2.pp1973-1981"},{"issue":"5","key":"10603_CR60","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1037\/0022-3514.90.5.862","volume":"90","author":"MR Mehl","year":"2006","unstructured":"Mehl MR, Gosling SD, Pennebaker JW (2006) Personality in its natural habitat: manifestations and implicit folk theories of personality in daily life. J Personality Soc Psychol 90(5):862","journal-title":"J Personality Soc Psychol"},{"key":"10603_CR61","doi-asserted-by":"crossref","unstructured":"Mehta Y, Fatehi S, Kazameini A, Stachl C, Cambria E, Eetemadi S (2020) Bottom-up and top-down: Predicting personality with psycholinguistic and language model features. pp 1184\u20131189","DOI":"10.1109\/ICDM50108.2020.00146"},{"key":"10603_CR62","unstructured":"Mohammad S, (2018) Word affect intensities. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA)"},{"key":"10603_CR63","doi-asserted-by":"crossref","unstructured":"Mohammadi G, Vinciarelli A (2015) Automatic personality perception: Prediction of trait attribution based on prosodic features extended abstract. In: 2015 international conference on affective computing and intelligent interaction (ACII), pp 484\u2013490. IEEE","DOI":"10.1109\/ACII.2015.7344614"},{"key":"10603_CR64","doi-asserted-by":"crossref","unstructured":"Moraes R, Pinto LL, Pilankar M, Rane P (2020) Personality assessment using social media for hiring candidates. In: 2020 3rd international conference on communication system, computing and IT applications (CSCITA), pp 192\u2013197. IEEE","DOI":"10.1109\/CSCITA47329.2020.9137818"},{"key":"10603_CR65","volume-title":"Explorations in personality: a clinical and experimental study of fifty men of college age","author":"H Murray","year":"1938","unstructured":"Murray H, Clinic HUHP (1938) Explorations in personality: a clinical and experimental study of fifty men of college age. Oxford University Press"},{"key":"10603_CR66","doi-asserted-by":"crossref","unstructured":"Nasir AFA, Nee ES, Choong CS, Ghani ASA, Majeed APA, Adam A, Furqan M (2020) Text-based emotion prediction system using machine learning approach. 769(1):012022","DOI":"10.1088\/1757-899X\/769\/1\/012022"},{"key":"10603_CR67","doi-asserted-by":"crossref","unstructured":"Nguyen-The M, Lamghari S, Bilodeau G-A, Rockemann J (2022) Leveraging sentiment analysis knowledge to solve emotion detection tasks. In: International conference on pattern recognition, pp 405\u2013416. Springer","DOI":"10.1007\/978-3-031-37660-3_29"},{"key":"10603_CR68","doi-asserted-by":"crossref","unstructured":"\u00d6hman E, P\u00e0mies M, Kajava K, Tiedemann J (2020) XED: A multilingual dataset for sentiment analysis and emotion detection. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 6542\u20136552, Barcelona, Spain (Online). International Committee on Computational Linguistics","DOI":"10.18653\/v1\/2020.coling-main.575"},{"issue":"6","key":"10603_CR69","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1037\/0022-3514.77.6.1296","volume":"77","author":"JW Pennebaker","year":"1999","unstructured":"Pennebaker JW, King LA (1999) Linguistic styles: language use as an individual difference. J Person Soc Psychol 77(6):1296","journal-title":"J Person Soc Psychol"},{"key":"10603_CR70","doi-asserted-by":"crossref","unstructured":"Poria S, Hazarika D, Majumder N, Naik G, Cambria E, Mihalcea R (2019) MELD: a multimodal multi-party dataset for emotion recognition in conversations. pp 527\u2013536","DOI":"10.18653\/v1\/P19-1050"},{"issue":"13","key":"10603_CR71","doi-asserted-by":"crossref","first-page":"7502","DOI":"10.3390\/app13137502","volume":"13","author":"L Rei","year":"2023","unstructured":"Rei L, Mladeni\u0107 D (2023) Detecting fine-grained emotions in literature. Appl Sci 13(13):7502","journal-title":"Appl Sci"},{"key":"10603_CR72","doi-asserted-by":"crossref","unstructured":"Reimers N, Gurevych I (2019) Sentence-BERT: Sentence embeddings using Siamese BERT-networks. pp 3982\u20133992","DOI":"10.18653\/v1\/D19-1410"},{"issue":"1","key":"10603_CR73","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.cogsys.2008.03.001","volume":"10","author":"R Reisenzein","year":"2009","unstructured":"Reisenzein R (2009) Emotions as metarepresentational states of mind: naturalizing the belief-desire theory of emotion. Cognit Syst Res 10(1):6\u201320","journal-title":"Cognit Syst Res"},{"issue":"3","key":"10603_CR74","volume":"58","author":"Z Ren","year":"2021","unstructured":"Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inform Process Manag 58(3):102532","journal-title":"Inform Process Manag"},{"issue":"4","key":"10603_CR75","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/j.1745-6916.2007.00047.x","volume":"2","author":"BW Roberts","year":"2007","unstructured":"Roberts BW, Kuncel NR, Shiner R, Caspi A, Goldberg LR (2007) The power of personality: the comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspectives Psychol Sci 2(4):313\u2013345","journal-title":"Perspectives Psychol Sci"},{"key":"10603_CR76","doi-asserted-by":"crossref","unstructured":"Rosenthal S, Farra N, Nakov P (2017) SemEval-2017 task 4: Sentiment analysis in Twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp 502\u2013518, Vancouver, Canada. Association for Computational Linguistics","DOI":"10.18653\/v1\/S17-2088"},{"issue":"1","key":"10603_CR77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00398-3","volume":"8","author":"M Rostami","year":"2021","unstructured":"Rostami M, Berahmand K, Forouzandeh S (2021) A novel community detection based genetic algorithm for feature selection. J Big Data 8(1):1\u201327","journal-title":"J Big Data"},{"key":"10603_CR78","unstructured":"Ruch Willibald, Wagner Lisa, Heintz Sonja (2018) Humor, the pen model of personality, and subjective well-being: Support for differential relationships of eight comic styles"},{"issue":"2","key":"10603_CR79","first-page":"169","volume":"9","author":"SS Sadeghi","year":"2021","unstructured":"Sadeghi SS, Khotanlou H, Rasekh Mahand M (2021) Automatic persian text emotion detection using cognitive linguistic and deep learning. J AI and Data Min 9(2):169\u2013179","journal-title":"J AI and Data Min"},{"key":"10603_CR80","unstructured":"Sadock BJ, Sadock VA, Ruiz P (2017) Kaplan and sadock\u2019s comprehensive textbook of psychiatry -. Wolters Kluwer Health"},{"key":"10603_CR81","doi-asserted-by":"crossref","unstructured":"Safari F,Chalechale A (2022) Classification of personality traits on facebook using key phrase extraction, language models and machine learning. In: 2022 13th international conference on information and knowledge technology (IKT), pp 1\u20135","DOI":"10.1109\/IKT57960.2022.10039020"},{"issue":"3","key":"10603_CR82","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/a15030087","volume":"15","author":"S Sagadevan","year":"2022","unstructured":"Sagadevan S, Malim NHAH, Husin MH (2022) A seed-guided latent dirichlet allocation approach to predict the personality of online users using the pen model. Algorithms 15(3):87","journal-title":"Algorithms"},{"issue":"2","key":"10603_CR83","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1037\/0022-3514.66.2.310","volume":"66","author":"KR Scherer","year":"1994","unstructured":"Scherer KR, Wallbott HG (1994) Evidence for universality and cultural variation of differential emotion response patterning. J Personality Soc Psychol 66(2):310","journal-title":"J Personality Soc Psychol"},{"issue":"2","key":"10603_CR84","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1037\/h0054570","volume":"61","author":"H Schlosberg","year":"1954","unstructured":"Schlosberg H (1954) Three dimensions of emotion. Psychol Rev 61(2):81","journal-title":"Psychol Rev"},{"key":"10603_CR85","doi-asserted-by":"crossref","unstructured":"Seal D, Roy UK, Basak R (2020) Sentence-level emotion detection from text based on semantic rules. pp 423\u2013430","DOI":"10.1007\/978-981-13-7166-0_42"},{"key":"10603_CR86","doi-asserted-by":"crossref","unstructured":"Setiawan H, Wafi AA (2020) Classification of personality type based on twitter data using machine learning techniques. In: 2020 3rd international conference on information and communications technology (ICOIACT), pp 94\u201398. IEEE","DOI":"10.1109\/ICOIACT50329.2020.9332152"},{"key":"10603_CR87","unstructured":"Shand AF (1920) The foundations of character: Being a study of the tendencies of the emotions and sentiments. Macmillan and Company"},{"key":"10603_CR88","doi-asserted-by":"crossref","unstructured":"Singh L, Singh S, Aggarwal N (2019) Two-stage text feature selection method for human emotion recognition. In: Proceedings of 2nd international conference on communication, computing and networking, pp 531\u2013538","DOI":"10.1007\/978-981-13-1217-5_51"},{"key":"10603_CR89","doi-asserted-by":"crossref","unstructured":"Sirasapalli JJ, Malla RM (2023) A deep learning approach to text-based personality prediction using multiple data sources mapping. Neural Comput Appl pp 1\u201312","DOI":"10.1007\/s00521-023-08846-w"},{"key":"10603_CR90","doi-asserted-by":"crossref","unstructured":"Sridhar BN, Mrinalini K, Vijayalakshmi P (2020) Data annotation and multi-emotion classification for social media text. In: 2020 international conference on communication and signal processing (ICCSP), pp 1011\u20131015. IEEE","DOI":"10.1109\/ICCSP48568.2020.9182362"},{"issue":"3","key":"10603_CR91","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1108\/00197850910950952","volume":"41","author":"J Sugerman","year":"2009","unstructured":"Sugerman J (2009) Using the disc\u00ae model to improve communication effectiveness. Ind Commercial Train 41(3):151\u2013154","journal-title":"Ind Commercial Train"},{"key":"10603_CR92","doi-asserted-by":"crossref","first-page":"61959","DOI":"10.1109\/ACCESS.2018.2876502","volume":"6","author":"MM Tadesse","year":"2018","unstructured":"Tadesse MM, Lin H, Xu B, Yang L (2018) Personality predictions based on user behavior on the facebook social media platform. IEEE Access 6:61959\u201361969","journal-title":"IEEE Access"},{"issue":"3","key":"10603_CR93","first-page":"283","volume":"9","author":"N Taghvaei","year":"2021","unstructured":"Taghvaei N, Masoumi B, Keyvanpour MR (2021) A hybrid framework for personality prediction based on fuzzy neural networks and deep neural networks. J AI Data Min 9(3):283\u2013294","journal-title":"J AI Data Min"},{"issue":"1","key":"10603_CR94","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s44196-023-00234-5","volume":"16","author":"M Tahir","year":"2023","unstructured":"Tahir M, Halim Z, Waqas M, Tu S (2023) On the effect of emotion identification from limited translated text samples using computational intelligence. Int J Comput Intell Syst 16(1):107","journal-title":"Int J Comput Intell Syst"},{"key":"10603_CR95","doi-asserted-by":"crossref","unstructured":"Teli MA, Chachoo MA (2023) Pre-trained word embeddings in deep multi-label personality classification of youtube transliterations. In: 2023 international conference on intelligent systems, advanced computing and communication (ISACC), pp 1\u20136. IEEE","DOI":"10.1109\/ISACC56298.2023.10084047"},{"key":"10603_CR96","volume":"235","author":"G Tu","year":"2022","unstructured":"Tu G, Wen J, Liu H, Chen S, Zheng L, Jiang D (2022) Exploration meets exploitation: Multitask learning for emotion recognition based on discrete and dimensional models. Knowl-Based Syst 235:107598","journal-title":"Knowl-Based Syst"},{"key":"10603_CR97","doi-asserted-by":"crossref","unstructured":"Usher J, Dondio P (2020) Brexit: Psychometric profiling the political salubrious through machine learning: Predicting personality traits of boris johnson through twitter political text. In: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, pp 178\u2013183","DOI":"10.1145\/3405962.3405981"},{"key":"10603_CR98","volume":"225","author":"HA Uymaz","year":"2023","unstructured":"Uymaz HA, Metin SK (2023) Emotion-enriched word embeddings for Turkish. Expert Syst Appl 225:120011","journal-title":"Expert Syst Appl"},{"key":"10603_CR99","unstructured":"Verhoeven B, Daelemans W, Plank B (2016) Twisty: a multilingual twitter stylometry corpus for gender and personality profiling. In: Proceedings of the 10th Annual Conference on Language Resources and Evaluation (LREC 2016)\/Calzolari, Nicoletta [edit.]; et al., pp 1\u20136"},{"issue":"18","key":"10603_CR100","doi-asserted-by":"crossref","first-page":"28349","DOI":"10.1007\/s11042-021-10997-8","volume":"80","author":"A Vijayvergia","year":"2021","unstructured":"Vijayvergia A, Kumar K (2021) Selective shallow models strength integration for emotion detection using glove and lstm. Multimedia Tools Appl 80(18):28349\u201328363","journal-title":"Multimedia Tools Appl"},{"key":"10603_CR101","unstructured":"Vitiugin F, Barnabo G (2021) Emotion detection for spanish by combining laser embeddings, topic information, and offense features. In: IberLEF@ SEPLN, pp 78\u201385"},{"key":"10603_CR102","unstructured":"Wang B, Liakata M, Zubiaga A, Procter R, Jensen E (2016) Smile: Twitter emotion classification using domain adaptation. In: 25th international joint conference on artificial intelligence, page\u00a015"},{"issue":"3","key":"10603_CR103","volume":"60","author":"Q Wang","year":"2023","unstructured":"Wang Q, Su T, Lau RYK, Xie H (2023) Deepemotionnet: Emotion mining for corporate performance analysis and prediction. Inform Process Manag 60(3):103151","journal-title":"Inform Process Manag"},{"key":"10603_CR104","doi-asserted-by":"crossref","DOI":"10.1037\/12908-000","volume-title":"Outline Psychol","author":"W Wundt","year":"1897","unstructured":"Wundt W (1897) Outline Psychol. Outline of psychology, Wilhelm Engelmann, Leipzig, Germany"},{"issue":"11","key":"10603_CR105","doi-asserted-by":"crossref","first-page":"7705","DOI":"10.1007\/s10489-021-02277-7","volume":"51","author":"X Xue","year":"2021","unstructured":"Xue X, Feng J, Sun X (2021) Semantic-enhanced sequential modeling for personality trait recognition from texts. Appl Intell 51(11):7705\u20137717","journal-title":"Appl Intell"},{"key":"10603_CR106","volume":"203","author":"D Yan","year":"2022","unstructured":"Yan D, Cao J, Xie W, Zhang Y, Zhong H (2022) Personalitygate: a general plug-and-play gnn gate to enhance cascade prediction with personality recognition task. Expert Syst Appl 203:117381","journal-title":"Expert Syst Appl"},{"key":"10603_CR107","doi-asserted-by":"crossref","first-page":"48","DOI":"10.5116\/ijme.5698.e2cd","volume":"7","author":"C Yang","year":"2016","unstructured":"Yang C, Richard G, Durkin M (2016) The association between myers-briggs type indicator and psychiatry as the specialty choice. Int J Med Educ 7:48","journal-title":"Int J Med Educ"},{"key":"10603_CR108","first-page":"13896","volume":"37","author":"T Yang","year":"2023","unstructured":"Yang T, Deng J, Quan X, Wang Q (2023) Orders are unwanted: dynamic deep graph convolutional network for personality detection. Proc AAAI Conf Artificial Intell 37:13896\u201313904","journal-title":"Proc AAAI Conf Artificial Intell"},{"key":"10603_CR109","doi-asserted-by":"crossref","unstructured":"Zhang S-y (2022) Deep learning method for human emotion detection and text analysis based on big data. In: International conference on cognitive based information processing and applications (CIPA 2021) Volume 1, pp 486\u2013490. Springer","DOI":"10.1007\/978-981-16-5857-0_62"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10603-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10603-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10603-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T20:40:42Z","timestamp":1730148042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10603-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,23]]},"references-count":109,"journal-issue":{"issue":"S3","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10603"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10603-3","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,23]]},"assertion":[{"value":"23 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}