{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:21:21Z","timestamp":1775326881784,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811958670","type":"print"},{"value":"9789811958687","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-19-5868-7_49","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T07:49:15Z","timestamp":1672559355000},"page":"671-677","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Comparative Analysis of\u00a0Lexicon-Based Emotion Recognition of\u00a0Text"],"prefix":"10.1007","author":[{"given":"Anima","family":"Pradhan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manas Ranjan","family":"Senapati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pradip Kumar","family":"Sahu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Abdul-Mageed M, Ungar L (2017) Emonet: fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th annual meeting of the association for computational linguistics, vol 1: Long papers, pp 718\u2013728","DOI":"10.18653\/v1\/P17-1067"},{"key":"49_CR2","doi-asserted-by":"crossref","unstructured":"Alm CO, Roth D, Sproat R (2005) Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the conference on human language technology and empirical methods in natural language processing, pp 579\u2013586","DOI":"10.3115\/1220575.1220648"},{"issue":"1","key":"49_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1006\/obhd.1999.2838","volume":"79","author":"R Raghunathan","year":"1999","unstructured":"Raghunathan R, Pham MT (1999) All negative moods are not equal: motivational influences of anxiety and sadness on decision making. Organ Behav Hum Decis Process 79(1):56\u201377","journal-title":"Organ Behav Hum Decis Process"},{"issue":"1","key":"49_CR4","first-page":"1","volume":"17","author":"R Meo","year":"2017","unstructured":"Meo R, Sulis E (2017) Processing affect in social media: a comparison of methods to distinguish emotions in Tweets. ACM Trans Internet Technology (TOIT) 17(1):1\u201325","journal-title":"ACM Trans Internet Technology (TOIT)"},{"issue":"3","key":"49_CR5","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","volume":"29","author":"SM Mohammad","year":"2013","unstructured":"Mohammad SM, Turney P (2013) Crowdsourcing a word-emotion association lexicon. Comput Intell 29(3):436\u2013465","journal-title":"Comput Intell"},{"issue":"1","key":"49_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1\u20138","journal-title":"J Comput Sci"},{"key":"49_CR7","unstructured":"Mohammad SM, Yang TW (2013) Tracking sentiment in mail: how genders differ on emotional axes. In: Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis. Association for computational linguistics, pp 70\u201379"},{"issue":"1\u20132","key":"49_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/1500000011","volume":"2","author":"B Pang","year":"2008","unstructured":"Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retrieval 2(1\u20132):1\u2013135","journal-title":"Found Trends Inf Retrieval"},{"issue":"4","key":"49_CR9","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1177\/0092070303254412","volume":"31","author":"R Bougie","year":"2003","unstructured":"Bougie R, Pieters R, Zeelenberg M (2003) Angry customers don\u2019t come back, they get back: the experience and behavioral implications of anger and dissatisfaction in services. J Acad Mark Sci 31(4):377\u2013393","journal-title":"J Acad Mark Sci"},{"key":"49_CR10","unstructured":"Plutchik R (1994) The psychology and biology of emotion. HarperCollins College Publishers"},{"issue":"3\u20134","key":"49_CR11","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02699939208411068","volume":"6","author":"P Ekman","year":"1992","unstructured":"Ekman P (1992) An argument for basic emotions. Cogn Emotion 6(3\u20134):169\u2013200","journal-title":"Cogn Emotion"},{"key":"49_CR12","unstructured":"Strapparava C, Valitutti A (2004) Wordnet affect: an affective extension of wordnet. In Lrec, vol 4, pp 1083\u20131086"},{"issue":"4","key":"49_CR13","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1016\/j.ipm.2014.09.003","volume":"51","author":"SM Mohammad","year":"2015","unstructured":"Mohammad SM, Zhu X, Kiritchenko S, Martin J (2015) Sentiment, emotion, purpose, and style in electoral tweets. Inf Process Manag 51(4):480\u2013499","journal-title":"Inf Process Manag"},{"key":"49_CR14","doi-asserted-by":"crossref","unstructured":"Koumpouri A, Mporas I, Megalooikonomou V (2015) Evaluation of four approaches for \u201cSentiment Analysis on Movie Reviews\u201d The Kaggle competition. In: Proceedings of the 16th international conference on engineering applications of neural networks (INNS), pp 1\u20135","DOI":"10.1145\/2797143.2797182"},{"key":"49_CR15","unstructured":"Krishnan H, Elayidom MS, Santhanakrishnan T (2017) Emotion detection of Tweets using Na\u00efve Bayes Classifier. Int J Eng Technol Sci Res 4(11):457\u2013462"},{"key":"49_CR16","doi-asserted-by":"crossref","unstructured":"Strapparava C, Mihalcea R (2008) Learning to identify emotions in text. In: Proceedings of the 2008 ACM symposium on applied computing, pp 1556\u20131560","DOI":"10.1145\/1363686.1364052"},{"issue":"4","key":"49_CR17","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1016\/j.eswa.2013.08.073","volume":"41","author":"W Li","year":"2014","unstructured":"Li W, Xu H (2014) Text-based emotion classification using emotion cause extraction. Expert Syst Appl 41(4):1742\u20131749","journal-title":"Expert Syst Appl"},{"key":"49_CR18","unstructured":"Roberts K, Roach MA, Johnson J, Guthrie J, Harabagiu SM (2012) EmpaTweet: annotating and detecting emotions on Twitter. In: Lrec, vol 12, pp 3806\u20133813"},{"issue":"12","key":"49_CR19","doi-asserted-by":"publisher","first-page":"2544","DOI":"10.1002\/asi.21416","volume":"61","author":"T Mike","year":"2010","unstructured":"Mike T, Kevan B, Georgios P, Di C, Arvid K (2010) Sentiment in short strength detection informal text. J Am Soc Inf Sci Technol 61(12):2544\u20132558","journal-title":"J Am Soc Inf Sci Technol"},{"issue":"Suppl. 1","key":"49_CR20","first-page":"61","volume":"5","author":"K Luyckx","year":"2012","unstructured":"Luyckx K, Vaassen F, Peersman C, Daelemans W (2012) Fine-grained emotion detection in suicide notes: a thresholding approach to multi-label classification. Biomed Inf Insights 5(Suppl. 1):61\u201369","journal-title":"Biomed Inf Insights"},{"key":"49_CR21","doi-asserted-by":"crossref","unstructured":"Staiano J, Guerini M (2014) Depechemood: a lexicon for emotion analysis from crowd-annotated news. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, association for computational linguistics, pp 427\u2013433","DOI":"10.3115\/v1\/P14-2070"},{"key":"49_CR22","doi-asserted-by":"crossref","unstructured":"Araque O, Gatti L, Staiano J, Guerini M (2019) Depechemood++: a bilingual emotion lexicon built through simple yet powerful techniques. IEEE Trans Affect Comput 13(1):496\u2013507","DOI":"10.1109\/TAFFC.2019.2934444"}],"container-title":["Lecture Notes in Electrical Engineering","Machine Learning, Image Processing, Network Security and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-5868-7_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T08:47:49Z","timestamp":1672562869000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5868-7_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811958670","9789811958687"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5868-7_49","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"value":"1876-1100","type":"print"},{"value":"1876-1119","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}