{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T15:40:06Z","timestamp":1747582806447,"version":"3.40.5"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T00:00:00Z","timestamp":1716595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C2093785","2021R1A2C2093785","2021R1A2C2093785"],"award-info":[{"award-number":["2021R1A2C2093785","2021R1A2C2093785","2021R1A2C2093785"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Lang Resources &amp; Evaluation"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Explainability, which is the degree to which an interested stakeholder can understand the key factors that led to a data-driven model\u2019s decision, has been considered an essential consideration in the financial domain. Accordingly, lexicons that can achieve reasonable performance and provide clear explanations to users have been among the most popular resources in sentiment-based financial forecasting. Since deep learning-based techniques have limitations in that the basis for interpreting the results is unclear, lexicons have consistently attracted the community\u2019s attention as a crucial tool in studies that demand explanations for the sentiment estimation process. One of the challenges in the construction of a financial sentiment lexicon is the domain-specific feature that the sentiment orientation of a word can change depending on the application of directional expressions. For instance, the word \u201ccost\u201d typically conveys a negative sentiment; however, when the word is juxtaposed with \u201cdecrease\u201d to form the phrase \u201ccost decrease,\u201d the associated sentiment is positive. Several studies have manually built lexicons containing directional expressions. However, they have been hindered because manual inspection inevitably requires intensive human labor and time. In this study, we propose to automatically construct the \u201csentiment lexicon composed of direction-dependent words,\u201d which expresses each term as a pair consisting of a directional word and a direction-dependent word. Experimental results show that the proposed sentiment lexicon yields enhanced classification performance, proving the effectiveness of our method for the automated construction of a direction-aware sentiment lexicon.<\/jats:p>","DOI":"10.1007\/s10579-024-09737-9","type":"journal-article","created":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T09:01:22Z","timestamp":1716627682000},"page":"843-869","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic construction of direction-aware sentiment lexicon using direction-dependent words"],"prefix":"10.1007","volume":"59","author":[{"given":"Jihye","family":"Park","sequence":"first","affiliation":[]},{"given":"Hye Jin","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Sungzoon","family":"Cho","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"issue":"3","key":"9737_CR1","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/s10579-016-9364-5","volume":"51","author":"A Abdaoui","year":"2017","unstructured":"Abdaoui, A., Az\u00e9, J., Bringay, S., & Poncelet, P. (2017). Feel: A French expanded emotion lexicon. Language Resources and Evaluation, 51(3), 833\u2013855.","journal-title":"Language Resources and Evaluation"},{"key":"9737_CR2","unstructured":"Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the 7th conference on international language resources and evaluation (pp. 2200\u20132204)."},{"issue":"1","key":"9737_CR3","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/MIS.2017.22","volume":"32","author":"A Bandhakavi","year":"2017","unstructured":"Bandhakavi, A., Wiratunga, N., Massie, S., & Padmanabhan, D. (2017). Lexicon generation for emotion detection from text. IEEE Intelligent Systems, 32(1), 102\u2013108.","journal-title":"IEEE Intelligent Systems"},{"key":"9737_CR4","volume-title":"Natural language processing with python: Analyzing text with the natural language toolkit","author":"S Bird","year":"2009","unstructured":"Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with python: Analyzing text with the natural language toolkit. O\u2019Reilly."},{"key":"9737_CR5","doi-asserted-by":"crossref","unstructured":"Bracke, P., Datta, A., Jung, C., & Sen, S. (2019). Machine learning explainability in finance: An application to default risk analysis. Bank of England Working Paper. Retrieved January 6, 2024, from https:\/\/www.bankofengland.co.uk\/working-paper\/2019\/machine-learning-explainability-in-finance-an-application-to-default-risk-analysis","DOI":"10.2139\/ssrn.3435104"},{"key":"9737_CR6","unstructured":"Brazdil, P., Silvano, P., Silva, F., Muhammad, S., Oliveira, F., Cordeiro, J., & Leal, A. (2022). Extending general sentiment lexicon to specific domains in (semi-) automatic manner. In 1st Workshop on sentiment analysis & linguistic linked data: Proceedings of the workshops and tutorials held at LDK 2021 co-located with the 3rd language, data and knowledge conference."},{"issue":"1","key":"9737_CR7","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10614-020-10042-0","volume":"57","author":"N Bussmann","year":"2021","unstructured":"Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57(1), 203\u2013216.","journal-title":"Computational Economics"},{"issue":"2","key":"9737_CR8","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s12559-015-9325-0","volume":"7","author":"E Cambria","year":"2015","unstructured":"Cambria, E., & Hussain, A. (2015). Sentic computing. Cognitive Computation, 7(2), 183\u2013185.","journal-title":"Cognitive Computation"},{"issue":"4","key":"9737_CR9","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.eeh.2012.07.002","volume":"49","author":"G Campbell","year":"2012","unstructured":"Campbell, G., Turner, J. D., & Walker, C. B. (2012). The role of the media in a bubble. Explorations in Economic History, 49(4), 461\u2013481.","journal-title":"Explorations in Economic History"},{"issue":"4","key":"9737_CR10","first-page":"5","volume":"100","author":"S Cannon","year":"2015","unstructured":"Cannon, S. (2015). Sentiment of the FOMC: Unscripted. Economic Review - Federal Reserve Bank of Kansas City, 100(4), 5\u201331.","journal-title":"Economic Review - Federal Reserve Bank of Kansas City"},{"issue":"14","key":"9737_CR11","doi-asserted-by":"publisher","first-page":"2437","DOI":"10.3390\/math10142437","volume":"10","author":"WK Cheng","year":"2022","unstructured":"Cheng, W. K., Bea, K. T., Leow, S. M. H., Chan, J. Y.-L., Hong, Z.-W., & Chen, Y.-L. (2022). A review of sentiment, semantic and event-extraction-based approaches in stock forecasting. Mathematics, 10(14), 2437.","journal-title":"Mathematics"},{"key":"9737_CR12","doi-asserted-by":"crossref","unstructured":"Choi, S., Park, H., Yeo, J., & Hwang, S.-W. (2020). Less is more: Attention supervision with counterfactuals for text classification. In Proceedings of the 2020 conference on empirical methods in natural language processing (pp. 6695\u20136704).","DOI":"10.18653\/v1\/2020.emnlp-main.543"},{"key":"9737_CR13","doi-asserted-by":"crossref","unstructured":"Cortis, K., Freitas, A., Daudert, T., Huerlimann, M., Zarrouk, M., Handschuh, S., & Davis, B. (2017). Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news (pp. 519\u2013535). Association for Computational Linguistics (ACL). Retrieved January 6, 2024, from https:\/\/alt.qcri.org\/semeval2017\/task5\/index.php","DOI":"10.18653\/v1\/S17-2089"},{"issue":"3","key":"9737_CR14","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/s10579-015-9307-6","volume":"50","author":"R Dehkharghani","year":"2016","unstructured":"Dehkharghani, R., Saygin, Y., Yanikoglu, B., & Oflazer, K. (2016). SentiTurkNet: A Turkish polarity lexicon for sentiment analysis. Language Resources and Evaluation, 50(3), 667\u2013685.","journal-title":"Language Resources and Evaluation"},{"key":"9737_CR15","unstructured":"Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pretraining of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American Chapter of the Association for Computational Linguistics: Human language technologies (pp. 4171\u20134186)."},{"issue":"3","key":"9737_CR16","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1093\/rfs\/hhr133","volume":"25","author":"C Dougal","year":"2012","unstructured":"Dougal, C., Engelberg, J., Garcia, D., & Parsons, C. A. (2012). Journalists and the stock market. The Review of Financial Studies, 25(3), 639\u2013679.","journal-title":"The Review of Financial Studies"},{"issue":"1","key":"9737_CR17","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1111\/j.1540-6261.2010.01626.x","volume":"66","author":"JE Engelberg","year":"2011","unstructured":"Engelberg, J. E., & Parsons, C. A. (2011). The causal impact of media in financial markets. The Journal of Finance, 66(1), 67\u201397.","journal-title":"The Journal of Finance"},{"key":"9737_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117605","volume":"205","author":"Z Feng","year":"2022","unstructured":"Feng, Z., Zhou, H., Zhu, Z., & Mao, K. (2022). Tailored text augmentation for sentiment analysis. Expert Systems with Applications, 205, 117605.","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"9737_CR19","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1111\/jofi.12027","volume":"68","author":"D Garcia","year":"2013","unstructured":"Garcia, D. (2013). Sentiment during recessions. The Journal of Finance, 68(3), 1267\u20131300.","journal-title":"The Journal of Finance"},{"issue":"14","key":"9737_CR20","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1080\/1351847X.2020.1743734","volume":"26","author":"AJ Hanna","year":"2020","unstructured":"Hanna, A. J., Turner, J. D., & Walker, C. B. (2020). News media and investor sentiment during bull and bear markets. The European Journal of Finance, 26(14), 1377\u20131395.","journal-title":"The European Journal of Finance"},{"key":"9737_CR21","doi-asserted-by":"crossref","unstructured":"Hutto, C., & Gilbert, E. (2014). 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 (pp. 216\u2013225).","DOI":"10.1609\/icwsm.v8i1.14550"},{"key":"9737_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-88443","volume-title":"Introduction to transformers for NLP","author":"SM Jain","year":"2022","unstructured":"Jain, S. M. (2022). Introduction to transformers for NLP. Apress. https:\/\/doi.org\/10.1007\/978-1-4842-88443"},{"key":"9737_CR23","unstructured":"Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In 3rd International conference on learning representations."},{"issue":"2","key":"9737_CR24","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10115-017-1134-1","volume":"56","author":"S Krishnamoorthy","year":"2018","unstructured":"Krishnamoorthy, S. (2018). Sentiment analysis of financial news articles using performance indicators. Knowledge and Information Systems, 56(2), 373\u2013394.","journal-title":"Knowledge and Information Systems"},{"issue":"5","key":"9737_CR25","doi-asserted-by":"publisher","first-page":"527","DOI":"10.5430\/ijfr.v11n5p527","volume":"11","author":"J-H Li","year":"2020","unstructured":"Li, J.-H., You, C.-F., Huang, & C.-S. (2020). Do mutual fund managers time market sentiment? International Journal of Financial Research, 11(5), 527\u2013537.","journal-title":"International Journal of Financial Research"},{"key":"9737_CR26","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.knosys.2014.04.022","volume":"69","author":"X Li","year":"2014","unstructured":"Li, X., Xie, H., Chen, L., Wang, J., & Deng, X. (2014). News impact on stock price return via sentiment analysis. Knowledge-Based Systems, 69, 14\u201323.","journal-title":"Knowledge-Based Systems"},{"key":"9737_CR27","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint. arXiv:1907.11692"},{"issue":"1","key":"9737_CR28","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1111\/j.1540-6261.2010.01625.x","volume":"66","author":"T Loughran","year":"2011","unstructured":"Loughran, T., & McDonald, B. (2011). When is a liability not a liability? textual analysis, dictionaries, and 10-ks. The Journal of Finance, 66(1), 35\u201365.","journal-title":"The Journal of Finance"},{"key":"9737_CR29","doi-asserted-by":"crossref","unstructured":"Maia, M., Handschuh, S., Freitas, A., Davis, B., McDermott, R., Zarrouk, M., & Balahur, A. (2018). WWW\u201918 Open Challenge: Financial Opinion Mining and Question Answering. In Companion proceedings of the the web conference 2018 (pp. 1941\u20131942). Retrieved January 6, 2024, from https:\/\/sites.google.com\/view\/fiqa\/home","DOI":"10.1145\/3184558.3192301"},{"issue":"4","key":"9737_CR30","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1002\/asi.23062","volume":"65","author":"P Malo","year":"2014","unstructured":"Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014). Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4), 782\u2013796.","journal-title":"Journal of the Association for Information Science and Technology"},{"issue":"203203","key":"9737_CR31","volume":"8","author":"A Mashrur","year":"2020","unstructured":"Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: A survey. IEEE Access, 8(203203), 203223.","journal-title":"IEEE Access"},{"key":"9737_CR32","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (Vol. 26, pp. 3111\u20133119). Curran Associates."},{"issue":"131662","key":"9737_CR33","volume":"8","author":"K Mishev","year":"2020","unstructured":"Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L. T., & Trajanov, D. (2020). Evaluation of sentiment analysis in finance: From lexicons to transformers. IEEE Access, 8(131662), 131682.","journal-title":"IEEE Access"},{"key":"9737_CR34","unstructured":"Mohammad, S., Kiritchenko, S., & Zhu, X. (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. In Proceedings of the 7th international workshop on semantic evaluation (pp. 321\u2013327)."},{"key":"9737_CR35","unstructured":"Moreno-Ortiz, A., Fern\u00e1ndez-Cruz, J., & Hern\u00e1ndez, C. P. C. (2020). Design and evaluation of SentiEcon: A fine-grained economic\/financial sentiment lexicon from a corpus of business news. In Proceedings of the 12th language resources and evaluation conference (pp. 5065\u20135072)."},{"key":"9737_CR36","unstructured":"Nielsen, F. \u00c5. (2011). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. In Proceedings of the ESWC2011 workshop on \u201cMaking Sense of Microposts\u201d: Big things come in small packages (Vol. 718, pp. 93\u201398)."},{"issue":"62","key":"9737_CR37","first-page":"73","volume":"85","author":"N Oliveira","year":"2016","unstructured":"Oliveira, N., Cortez, P., & Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85(62), 73.","journal-title":"Decision Support Systems"},{"key":"9737_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.eswa.2019.06.014","volume":"135","author":"A Picasso","year":"2019","unstructured":"Picasso, A., Merello, S., Ma, Y., Oneto, L., & Cambria, E. (2019). Technical analysis and sentiment embeddings for market trend prediction. Expert Systems with Applications, 135, 60\u201370.","journal-title":"Expert Systems with Applications"},{"key":"9737_CR39","doi-asserted-by":"crossref","unstructured":"Pramana, R., Subroto, J. J., Gunawan, A. A. S., & Anderies. (2022). Systematic literature review of stemming and lemmatization performance for sentence similarity. In 2022 IEEE 7th international conference on information technology and digital applications (ICITDA) (pp. 1\u20136).","DOI":"10.1109\/ICITDA55840.2022.9971451"},{"key":"9737_CR40","doi-asserted-by":"crossref","unstructured":"Razova, E., Vychegzhanin, S., & Kotelnikov, E. (2022). Does BERT look at sentiment lexicon? In International conference on analysis of images, social networks and texts (pp. 55\u201367).","DOI":"10.1007\/978-3-031-15168-2_6"},{"key":"9737_CR41","unstructured":"Ruiz-Mart\u00ednez, J. M., Valencia-Garc\u00eda, R., & Garc\u00eda-S\u00e1nchez, F. (2012). Semantic-based sentiment analysis in financial news. In Proceedings of the 1st international workshop on finance and economics on the semantic web (pp. 38\u201351)."},{"key":"9737_CR42","doi-asserted-by":"publisher","DOI":"10.1515\/9781400865536","volume-title":"Irrational exuberance","author":"RJ Shiller","year":"2016","unstructured":"Shiller, R. J. (2016). Irrational exuberance. Princeton University Press. https:\/\/doi.org\/10.1515\/9781400865536"},{"issue":"4","key":"9737_CR43","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1002\/bs.3830070412","volume":"7","author":"PJ Stone","year":"1962","unstructured":"Stone, P. J., Bales, R. F., Namenwirth, J. Z., & Ogilvie, D. M. (1962). The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science, 7(4), 484.","journal-title":"Behavioral Science"},{"issue":"2","key":"9737_CR44","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1162\/COLI_a_00049","volume":"37","author":"M Taboada","year":"2011","unstructured":"Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267\u2013307.","journal-title":"Computational Linguistics"},{"key":"9737_CR45","first-page":"2152","volume-title":"Proceedings of the 9th international conference on language resources and evaluation (LREC\u201914)","author":"P Takala","year":"2014","unstructured":"Takala, P., Malo, P., Sinha, A., & Ahlgren, O. (2014). Gold-standard for topic-specific sentiment analysis of economic texts. In N. Calzolari (Ed.), Proceedings of the 9th international conference on language resources and evaluation (LREC\u201914) (pp. 2152\u20132157). European Language Resources Association (ELRA)."},{"issue":"3","key":"9737_CR46","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1111\/j.1540-6261.2007.01232.x","volume":"62","author":"PC Tetlock","year":"2007","unstructured":"Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139\u20131168.","journal-title":"The Journal of Finance"},{"issue":"12","key":"9737_CR47","first-page":"2544","volume":"61","author":"M Thelwall","year":"2010","unstructured":"Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the Association for Information Science and Technology, 61(12), 2544\u20132558.","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"9737_CR48","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.inffus.2018.03.007","volume":"44","author":"A Valdivia","year":"2018","unstructured":"Valdivia, A., Luz\u00f3n, M. V., Cambria, E., & Herrera, F. (2018). Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion, 44, 126\u2013135.","journal-title":"Information Fusion"},{"issue":"11","key":"9737_CR49","doi-asserted-by":"publisher","first-page":"4999","DOI":"10.1016\/j.eswa.2015.02.007","volume":"42","author":"M Van de Kauter","year":"2015","unstructured":"Van de Kauter, M., Breesch, D., & Hoste, V. (2015). Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Systems with Applications, 42(11), 4999\u20135010.","journal-title":"Expert Systems with Applications"},{"key":"9737_CR50","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (Vol. 30). Curran Associates."},{"key":"9737_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117869","volume":"206","author":"D Vidanagama","year":"2022","unstructured":"Vidanagama, D., Silva, A., & Karunananda, A. (2022). Ontology based sentiment analysis for fake review detection. Expert Systems with Applications, 206, 117869.","journal-title":"Expert Systems with Applications"},{"issue":"04","key":"9737_CR52","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1142\/S0218488520500294","volume":"28","author":"Z Wang","year":"2020","unstructured":"Wang, Z., Ho, S.-B., & Cambria, E. (2020). Multi-level fine-scaled sentiment sensing with ambivalence handling. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 28(04), 683\u2013697.","journal-title":"International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"},{"key":"9737_CR53","doi-asserted-by":"crossref","unstructured":"Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (pp. 347\u2013354).","DOI":"10.3115\/1220575.1220619"},{"issue":"3","key":"9737_CR54","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s10579-018-9416-0","volume":"52","author":"L Wu","year":"2018","unstructured":"Wu, L., Morstatter, F., & Liu, H. (2018). SlangSD: Building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification. Language Resources and Evaluation, 52(3), 839\u2013852.","journal-title":"Language Resources and Evaluation"},{"key":"9737_CR55","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.eswa.2018.09.024","volume":"116","author":"S Wu","year":"2019","unstructured":"Wu, S., Wu, F., Chang, Y., Wu, C., & Huang, Y. (2019). Automatic construction of target-specific sentiment lexicon. Expert Systems with Applications, 116, 285\u2013298.","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"9737_CR56","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1109\/MCI.2018.2866727","volume":"13","author":"FZ Xing","year":"2018","unstructured":"Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Intelligent asset allocation via market sentiment views. IEEE Computational Intelligence Magazine, 13(4), 25\u201334.","journal-title":"IEEE Computational Intelligence Magazine"},{"key":"9737_CR57","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.knosys.2019.03.029","volume":"176","author":"FZ Xing","year":"2019","unstructured":"Xing, F. Z., Cambria, E., & Zhang, Y. (2019). Sentiment-aware volatility forecasting. Knowledge-Based Systems, 176, 68\u201376.","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"9737_CR58","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s10844-020-00630-9","volume":"57","author":"M Yekrangi","year":"2021","unstructured":"Yekrangi, M., & Abdolvand, N. (2021). Financial markets sentiment analysis: Developing a specialized lexicon. Journal of Intelligent Information Systems, 57(1), 127\u2013146.","journal-title":"Journal of Intelligent Information Systems"},{"key":"9737_CR59","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.knosys.2013.01.001","volume":"41","author":"L-C Yu","year":"2013","unstructured":"Yu, L.-C., Wu, J.-L., Chang, P.-C., & Chu, H.-S. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems, 41, 89\u201397.","journal-title":"Knowledge-Based Systems"}],"container-title":["Language Resources and Evaluation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-024-09737-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10579-024-09737-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10579-024-09737-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T15:04:14Z","timestamp":1747580654000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10579-024-09737-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,25]]},"references-count":59,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["9737"],"URL":"https:\/\/doi.org\/10.1007\/s10579-024-09737-9","relation":{},"ISSN":["1574-020X","1574-0218"],"issn-type":[{"type":"print","value":"1574-020X"},{"type":"electronic","value":"1574-0218"}],"subject":[],"published":{"date-parts":[[2024,5,25]]},"assertion":[{"value":"28 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}