{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:24:50Z","timestamp":1775139890451,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62066009"],"award-info":[{"award-number":["No.62066009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Research and Development Project of Guilin","award":["No. 2020010308"],"award-info":[{"award-number":["No. 2020010308"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s12559-022-10043-1","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T02:02:20Z","timestamp":1658109740000},"page":"254-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Automatically Constructing a Fine-Grained Sentiment Lexicon for Sentiment Analysis"],"prefix":"10.1007","volume":"15","author":[{"given":"Yabing","family":"Wang","sequence":"first","affiliation":[]},{"given":"Guimin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Maolin","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yiqun","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiaowei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"10043_CR1","doi-asserted-by":"crossref","unstructured":"Cambria E, Kumar A, Al-Ayyoub M, Howard N. Guest Editorial: explainable artificial intelligence for sentiment analysis. Elsevier; 2021.","DOI":"10.1016\/j.knosys.2021.107920"},{"key":"10043_CR2","doi-asserted-by":"crossref","unstructured":"Liang B, Su H, Gui L, Cambria E, Xu R. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl-Based Syst.\u00a02022;235:107643.","DOI":"10.1016\/j.knosys.2021.107643"},{"issue":"3","key":"10043_CR3","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 PD. Crowdsourcing a word-emotion association lexicon. Comput Intell. 2013;29(3):436\u201365.","journal-title":"Comput Intell"},{"key":"10043_CR4","unstructured":"Mohammad SM. Word affect intensities. arXiv preprint arXiv:1704.08798.\u00a02017."},{"key":"10043_CR5","doi-asserted-by":"crossref","unstructured":"Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing.\u00a02005. p. 347\u201354.","DOI":"10.3115\/1220575.1220619"},{"key":"10043_CR6","unstructured":"Stone PJ, Dunphy DC, Smith MS. The general inquirer: a computer approach to content analysis. 1966."},{"key":"10043_CR7","doi-asserted-by":"crossref","unstructured":"Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.\u00a02004. p. 168\u201377.","DOI":"10.1145\/1014052.1014073"},{"issue":"1","key":"10043_CR8","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s12559-021-09831-y","volume":"14","author":"F Bravo-Marquez","year":"2022","unstructured":"Bravo-Marquez F, Khanchandani A, Pfahringer B. Incremental word vectors for time-evolving sentiment lexicon induction. Cogn Comput. 2022;14(1):425\u201341.","journal-title":"Cogn Comput"},{"key":"10043_CR9","doi-asserted-by":"crossref","unstructured":"Sharma SS, Dutta G. Sentidraw: using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Inf Process Manag. 2021;58(1):102412.","DOI":"10.1016\/j.ipm.2020.102412"},{"key":"10043_CR10","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.ins.2020.02.026","volume":"520","author":"M Huang","year":"2020","unstructured":"Huang M, Xie H, Rao Y, Feng J, Wang FL. Sentiment strength detection with a context-dependent lexicon-based convolutional neural network. Inform Sci. 2020;520:389\u201399.","journal-title":"Inform Sci"},{"key":"10043_CR11","doi-asserted-by":"crossref","unstructured":"Viegas F, Alvim MS, Canuto S, Rosa T, Gon\u00e7alves MA, Rocha L. Exploiting semantic relationships for unsupervised expansion of sentiment lexicons. Inf Syst.\u00a02020;94:101606.","DOI":"10.1016\/j.is.2020.101606"},{"key":"10043_CR12","doi-asserted-by":"crossref","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. 2014.","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"10043_CR13","doi-asserted-by":"crossref","unstructured":"De Bruyne L, Atanasova P, Augenstein I. Joint emotion label space modeling for affect lexica. Comput Speech Lang. 2022;71:101257.","DOI":"10.1016\/j.csl.2021.101257"},{"key":"10043_CR14","doi-asserted-by":"crossref","unstructured":"Bandhakavi A, Wiratunga N, Massie S. Emotion-aware polarity lexicons for twitter sentiment analysis. Expert Syst. 2021;38(7):12332.","DOI":"10.1111\/exsy.12332"},{"key":"10043_CR15","doi-asserted-by":"publisher","first-page":"63359","DOI":"10.1109\/ACCESS.2020.2984284","volume":"8","author":"F Yin","year":"2020","unstructured":"Yin F, Wang Y, Liu J, Lin L. The construction of sentiment lexicon based on context-dependent part-of-speech chunks for semantic disambiguation. IEEE Access. 2020;8:63359\u201367.","journal-title":"IEEE Access"},{"key":"10043_CR16","doi-asserted-by":"crossref","unstructured":"Du M, Li X, Luo L. A training-optimization-based method for constructing domain-specific sentiment lexicon. Complexity. 2021;2021.","DOI":"10.1155\/2021\/6152494"},{"issue":"3\u20134","key":"10043_CR17","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/02699939208411068","volume":"6","author":"P Ekman","year":"1992","unstructured":"Ekman P. An argument for basic emotions. Cognit Emot. 1992;6(3\u20134):169\u2013200.","journal-title":"Cognit Emot"},{"key":"10043_CR18","unstructured":"James W. The emotions.\u00a01922."},{"key":"10043_CR19","doi-asserted-by":"crossref","unstructured":"Kilgarriff A. Wordnet: an electronic lexical database. JSTOR; 2000.","DOI":"10.2307\/417141"},{"issue":"2","key":"10043_CR20","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/coin.12024","volume":"31","author":"SM Mohammad","year":"2015","unstructured":"Mohammad SM, Kiritchenko S. Using hashtags to capture fine emotion categories from tweets. Comput Intell. 2015;31(2):301\u201326.","journal-title":"Comput Intell"},{"key":"10043_CR21","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 2013."},{"key":"10043_CR22","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD. Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).\u00a02014. p. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"issue":"3","key":"10043_CR23","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/TKDE.2007.48","volume":"19","author":"RL Cilibrasi","year":"2007","unstructured":"Cilibrasi RL, Vitanyi PM. The Google similarity distance. IEEE Trans Knowl Data Eng. 2007;19(3):370\u201383.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"6","key":"10043_CR24","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9","volume":"41","author":"S Deerwester","year":"1990","unstructured":"Deerwester S, Dumais ST, Furnas GW, Landauer TK, Harshman R. Indexing by latent semantic analysis. J Am Soc Inf Sci. 1990;41(6):391\u2013407.","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"10043_CR25","doi-asserted-by":"crossref","unstructured":"Strapparava C, Mihalcea R. Semeval-2007 task 14: affective text. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007).\u00a02007. p. 70\u20134.","DOI":"10.3115\/1621474.1621487"},{"key":"10043_CR26","doi-asserted-by":"crossref","unstructured":"Wang W, Chen L, Thirunarayan K, Sheth AP. Harnessing twitter \u201cbig data\u201d for automatic emotion identification. In: 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing. IEEE; 2012. p. 587\u201392.","DOI":"10.1109\/SocialCom-PASSAT.2012.119"},{"key":"10043_CR27","doi-asserted-by":"crossref","unstructured":"Bandhakavi A, Wiratunga N, Massie S, Deepak P. Emotion-corpus guided lexicons for sentiment analysis on twitter. In: International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer;\u00a02016. p. 71\u201385.","DOI":"10.1007\/978-3-319-47175-4_5"},{"key":"10043_CR28","doi-asserted-by":"crossref","unstructured":"Aman S, Szpakowicz S. Identifying expressions of emotion in text. In: International Conference on Text, Speech and Dialogue. Springer;\u00a02007. p. 196\u2013205.","DOI":"10.1007\/978-3-540-74628-7_27"},{"key":"10043_CR29","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L. Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. arXiv preprint cs\/0506075.\u00a02005.","DOI":"10.3115\/1219840.1219855"},{"key":"10043_CR30","doi-asserted-by":"crossref","unstructured":"Potts C. On the negativity of negation. In: Semantics and Linguistic Theory, vol. 20. 2010. p. 636\u201359.","DOI":"10.3765\/salt.v20i0.2565"},{"key":"10043_CR31","unstructured":"Nakov P, Kozareva Z, Ritter A, Rosenthal S, Stoyanov V, Wilson T. Semeval-2013 task 2: sentiment analysis in twitter.\u00a02013."},{"key":"10043_CR32","doi-asserted-by":"crossref","unstructured":"Staiano J, Guerini M. Depechemood: a lexicon for emotion analysis from crowd-annotated news. arXiv preprint arXiv:1405.1605.\u00a02014.","DOI":"10.3115\/v1\/P14-2070"},{"key":"10043_CR33","doi-asserted-by":"crossref","unstructured":"Badaro G, Jundi H, Hajj H, El-Hajj W. Emowordnet: automatic expansion of emotion lexicon using English wordnet. In: Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics.\u00a02018. p. 86\u201393.","DOI":"10.18653\/v1\/S18-2009"},{"key":"10043_CR34","doi-asserted-by":"crossref","unstructured":"Wang L, Xia R. Sentiment lexicon construction with representation learning based on hierarchical sentiment supervision. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.\u00a02017. p. 502\u201310.","DOI":"10.18653\/v1\/D17-1052"},{"key":"10043_CR35","unstructured":"Tang D, Wei F, Qin B, Zhou M, Liu T. Building large-scale twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of Coling 2014, the 25th International Conference on Computational Linguistics: Technical Papers.\u00a02014. p. 172\u201382."},{"key":"10043_CR36","doi-asserted-by":"crossref","unstructured":"Vo DT, Zhang Y. Don\u2019t count, predict! an automatic approach to learning sentiment lexicons for short text. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2.\u00a02016. p. 219\u201324.","DOI":"10.18653\/v1\/P16-2036"},{"key":"10043_CR37","unstructured":"Mohammad SM, Kiritchenko S, Zhu X. NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242.\u00a02013."},{"key":"10043_CR38","doi-asserted-by":"crossref","unstructured":"Suttles J, Ide N. Distant supervision for emotion classification with discrete binary values. In: International Conference on Intelligent Text Processing and Computational Linguistics. Springer;\u00a02013. p. 121\u201336.","DOI":"10.1007\/978-3-642-37256-8_11"},{"issue":"1","key":"10043_CR39","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1002\/asi.21662","volume":"63","author":"M Thelwall","year":"2012","unstructured":"Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. J Am Soc Inf Sci Technol. 2012;63(1):163\u201373.","journal-title":"J. Am. Soc. Inf. Sci. Technol."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10043-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-022-10043-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10043-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T10:43:09Z","timestamp":1678358589000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-022-10043-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["10043"],"URL":"https:\/\/doi.org\/10.1007\/s12559-022-10043-1","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,18]]},"assertion":[{"value":"12 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was not required as no humans or animals were involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}