{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T22:35:04Z","timestamp":1778279704631,"version":"3.51.4"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED481B-2021-118"],"award-info":[{"award-number":["ED481B-2021-118"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010801","name":"Xunta de Galicia","doi-asserted-by":"publisher","award":["ED481B-2022-093"],"award-info":[{"award-number":["ED481B-2022-093"]}],"id":[{"id":"10.13039\/501100010801","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006761","name":"Universidade de Vigo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006761","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006761","name":"Universidade de Vigo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006761","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (<jats:sc>nlp<\/jats:sc>) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (<jats:sc>lda<\/jats:sc>) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by <jats:sc>nlp<\/jats:sc> researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and <jats:sc>rouge-l<\/jats:sc> results endorse its potential as a valuable tool for busy investors. The <jats:sc>rouge-l<\/jats:sc> values for the identification of relevant text and predictions\/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with <jats:sc>lda<\/jats:sc> to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors\u2019 reputations.<\/jats:p>","DOI":"10.1007\/s10489-023-04452-4","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T19:03:24Z","timestamp":1678475004000},"page":"19610-19628","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0533-1303","authenticated-orcid":false,"given":"Silvia","family":"Garc\u00eda-M\u00e9ndez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"de Arriba-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana","family":"Barros-Vila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco J.","family":"Gonz\u00e1lez-Casta\u00f1o","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"Costa-Montenegro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"4452_CR1","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.future.2017.10.045","volume":"82","author":"G Manogaran","year":"2018","unstructured":"Manogaran G, Varatharajan R, Lopez D et al (2018) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Futur Gener Comput Syst 82:375\u2013387. https:\/\/doi.org\/10.1016\/j.future.2017.10.045","journal-title":"Futur Gener Comput Syst"},{"key":"4452_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4368036","volume":"2019","author":"V Deli\u0107","year":"2019","unstructured":"Deli\u0107 V, Peri\u0107 Z, Se\u010dujski M et al (2019) Speech technology progress based on new machine learning paradigm. Comput Intell Neurosci 2019:1\u201319. https:\/\/doi.org\/10.1155\/2019\/4368036","journal-title":"Comput Intell Neurosci"},{"issue":"6","key":"4452_CR3","doi-asserted-by":"publisher","first-page":"3226","DOI":"10.1007\/s10489-020-01915-w","volume":"51","author":"X Ma","year":"2020","unstructured":"Ma X, Fei Q, Qin H et al (2020) A new efficient decision making algorithm based on interval-valued fuzzy soft set. Appl Intell 51(6):3226\u20133240. https:\/\/doi.org\/10.1007\/s10489-020-01915-w","journal-title":"Appl Intell"},{"issue":"2","key":"4452_CR4","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1109\/TCCN.2020.2966615","volume":"6","author":"Y Zuo","year":"2020","unstructured":"Zuo Y, Wu Y, Min G et al (2020) An intelligent anomaly detection scheme for micro-services architectures with temporal and spatial data analysis. IEEE Trans Cogn Commun Netw 6(2):548\u2013561. https:\/\/doi.org\/10.1109\/TCCN.2020.2966615","journal-title":"IEEE Trans Cogn Commun Netw"},{"issue":"6","key":"4452_CR5","doi-asserted-by":"publisher","first-page":"e231","DOI":"10.2196\/jmir.9702","volume":"20","author":"TC Guetterman","year":"2018","unstructured":"Guetterman TC, Chang T, DeJonckheere M et al (2018) Augmenting qualitative text analysis with natural language processing: methodological study. J Med Int Res 20(6):e231. https:\/\/doi.org\/10.2196\/jmir.9702","journal-title":"J Med Int Res"},{"key":"4452_CR6","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.autcon.2018.12.016","volume":"99","author":"F Zhang","year":"2019","unstructured":"Zhang F, Fleyeh H, Wang X, et al. (2019) Construction site accident analysis using text mining and natural language processing techniques. Autom Constr 99:238\u2013248. https:\/\/doi.org\/10.1016\/j.autcon.2018.12.016","journal-title":"Autom Constr"},{"issue":"3","key":"4452_CR7","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s40593-020-00201-7","volume":"30","author":"R Balyan","year":"2020","unstructured":"Balyan R, McCarthy KS, McNamara DS (2020) Applying natural language processing and hierarchical machine learning approaches to text difficulty classification. Int J Artif Intell Educ 30(3):337\u2013370. https:\/\/doi.org\/10.1007\/s40593-020-00201-7","journal-title":"Int J Artif Intell Educ"},{"issue":"2","key":"4452_CR8","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1007\/s10489-021-02306-5","volume":"52","author":"X Lu","year":"2022","unstructured":"Lu X, Deng Y, Sun T et al (2022) MKPM: multi keyword-pair matching for natural language sentences. Appl Intell 52(2):1878\u20131892. https:\/\/doi.org\/10.1007\/s10489-021-02306-5","journal-title":"Appl Intell"},{"issue":"3","key":"4452_CR9","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1515\/jisys-2017-0520","volume":"28","author":"S Kumar","year":"2019","unstructured":"Kumar S, Kumar MA, Soman K (2019) Deep learning based part-of-speech tagging for Malayalam twitter data (special issue: deep learning techniques for natural language processing). J Intell Syst 28 (3):423\u2013435. https:\/\/doi.org\/10.1515\/jisys-2017-0520","journal-title":"J Intell Syst"},{"issue":"3","key":"4452_CR10","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.ipm.2018.01.008","volume":"54","author":"V K.","year":"2018","unstructured":"K. V, Gupta D (2018) Unmasking text plagiarism using syntactic-semantic based natural language processing techniques: comparisons, analysis and challenges. Inf Process Manag 54(3):408\u2013432. https:\/\/doi.org\/10.1016\/j.ipm.2018.01.008","journal-title":"Inf Process Manag"},{"issue":"1","key":"4452_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s10462-017-9588-9","volume":"50","author":"FZ Xing","year":"2018","unstructured":"Xing FZ, Cambria E, Welsch RE (2018) Natural language based financial forecasting: a survey. Artif Intell Rev 50(1):49\u201373. https:\/\/doi.org\/10.1007\/s10462-017-9588-9","journal-title":"Artif Intell Rev"},{"issue":"6","key":"4452_CR12","doi-asserted-by":"publisher","first-page":"102,055","DOI":"10.1016\/j.ipm.2019.102055","volume":"56","author":"A Lytos","year":"2019","unstructured":"Lytos A, Lagkas T, Sarigiannidis P et al (2019) The evolution of argumentation mining: from models to social media and emerging tools. Inf Process Manag 56(6):102,055. https:\/\/doi.org\/10.1016\/j.ipm.2019.102055","journal-title":"Inf Process Manag"},{"key":"4452_CR13","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.knosys.2018.03.004","volume":"150","author":"S Kelly","year":"2018","unstructured":"Kelly S, Ahmad K (2018) Estimating the impact of domain-specific news sentiment on financial assets. Knowl-Based Syst 150:116\u2013126. https:\/\/doi.org\/10.1016\/j.knosys.2018.03.004","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"4452_CR14","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.jfds.2018.02.002","volume":"4","author":"A Atkins","year":"2018","unstructured":"Atkins A, Niranjan M, Gerding E (2018) Financial news predicts stock market volatility better than close price. J Financ Data Sci 4(2):120\u2013137. https:\/\/doi.org\/10.1016\/j.jfds.2018.02.002","journal-title":"J Financ Data Sci"},{"issue":"2.29","key":"4452_CR15","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.14419\/ijet.v7i2.29.15146","volume":"7","author":"K Isa","year":"2018","unstructured":"Isa K, Rahman Ahmad A, Md Yusoff R et al (2018) NEWS analysis towards youth financial competency management. Int J Eng Technol 7(2.29):1151. https:\/\/doi.org\/10.14419\/ijet.v7i2.29.15146","journal-title":"Int J Eng Technol"},{"issue":"5","key":"4452_CR16","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1016\/j.ipm.2019.02.012","volume":"56","author":"H Zhang","year":"2019","unstructured":"Zhang H, Boons F, Batista-Navarro R (2019) Whose story is it anyway? Automatic extraction of accounts from news articles. Inf Process Manag 56(5):1837\u20131848. https:\/\/doi.org\/10.1016\/j.ipm.2019.02.012","journal-title":"Inf Process Manag"},{"key":"4452_CR17","doi-asserted-by":"publisher","first-page":"101,658","DOI":"10.1016\/j.frl.2020.101658","volume":"36","author":"CO Cepoi","year":"2020","unstructured":"Cepoi CO (2020) Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil. Financ Res Lett 36:101,658. https:\/\/doi.org\/10.1016\/j.frl.2020.101658","journal-title":"Financ Res Lett"},{"key":"4452_CR18","doi-asserted-by":"crossref","unstructured":"Swathi T, Kasiviswanath N, Rao AA (2022) An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis. Appl Intell :1\u201314","DOI":"10.1007\/s10489-022-03175-2"},{"issue":"4","key":"4452_CR19","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1111\/1475-679X.12123","volume":"54","author":"T Loughran","year":"2016","unstructured":"Loughran T, McDonald B (2016) Textual analysis in accounting and finance: a survey. J Account Res 54(4):1187\u20131230. https:\/\/doi.org\/10.1111\/1475-679X.12123","journal-title":"J Account Res"},{"key":"4452_CR20","doi-asserted-by":"publisher","first-page":"113,223","DOI":"10.1016\/j.eswa.2020.113223","volume":"148","author":"B Lutz","year":"2020","unstructured":"Lutz B, Pr\u00f6llochs N, Neumann D (2020) Predicting sentence-level polarity labels of financial news using abnormal stock returns. Exp Syst Appl 148:113,223. https:\/\/doi.org\/10.1016\/j.eswa.2020.113223","journal-title":"Exp Syst Appl"},{"issue":"4","key":"4452_CR21","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1016\/j.ipm.2019.04.003","volume":"56","author":"M Mohamed","year":"2019","unstructured":"Mohamed M, Oussalah M (2019) SRL-ESA-TextSum: a text summarization approach based on semantic role labeling and explicit semantic analysis. Inf Process Manag 56(4):1356\u20131372. https:\/\/doi.org\/10.1016\/j.ipm.2019.04.003","journal-title":"Inf Process Manag"},{"issue":"2","key":"4452_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5087\/dad.2017.201","volume":"8","author":"J Evers-Vermeul","year":"2017","unstructured":"Evers-Vermeul J, Hoek J, Scholman MC (2017) On temporality in discourse annotation: Theoretical and practical considerations. Dialogue Discourse 8(2):1\u201320. https:\/\/doi.org\/10.5087\/dad.2017.201","journal-title":"Dialogue Discourse"},{"issue":"12","key":"4452_CR23","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.3390\/electronics8121377","volume":"8","author":"Y Jang","year":"2019","unstructured":"Jang Y, Park CH, Seo YS (2019) Fake news analysis modeling using quote retweet. Electronics 8(12):1377. https:\/\/doi.org\/10.3390\/electronics8121377","journal-title":"Electronics"},{"issue":"16","key":"4452_CR24","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1136\/bjsports-2018-099432","volume":"53","author":"JY Chau","year":"2019","unstructured":"Chau JY, Reyes-Marcelino G, Burnett AC et al (2019) Hyping health effects: a news analysis of the \u2018new smoking\u2019 and the role of sitting. Br J Sports Med 53(16):1039\u20131040. https:\/\/doi.org\/10.1136\/bjsports-2018-099432","journal-title":"Br J Sports Med"},{"issue":"17","key":"4452_CR25","doi-asserted-by":"publisher","first-page":"2093","DOI":"10.1080\/13683500.2019.1618249","volume":"23","author":"GT Phi","year":"2020","unstructured":"Phi GT (2020) Framing overtourism: a critical news media analysis. Curr Issues Tour 23 (17):2093\u20132097. https:\/\/doi.org\/10.1080\/13683500.2019.1618249","journal-title":"Curr Issues Tour"},{"key":"4452_CR26","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.ins.2018.03.050","volume":"450","author":"Y Li","year":"2018","unstructured":"Li Y, Pan Q, Wang S et al (2018) A Generative model for category text generation. Inf Sci 450:301\u2013315. https:\/\/doi.org\/10.1016\/j.ins.2018.03.050","journal-title":"Inf Sci"},{"key":"4452_CR27","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.eswa.2018.10.008","volume":"118","author":"W Long","year":"2019","unstructured":"Long W, Song L, Tian Y (2019) A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity. Exp Syst Appl 118:411\u2013424. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.008","journal-title":"Exp Syst Appl"},{"issue":"2","key":"4452_CR28","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.ipm.2018.01.006","volume":"56","author":"M Al-Smadi","year":"2019","unstructured":"Al-Smadi M, Al-Ayyoub M, Jararweh Y et al (2019) Enhancing aspect-based sentiment analysis of Arabic Hotels\u2019 reviews using morphological, syntactic and semantic features. Inf Process Manag 56(2):308\u2013319. https:\/\/doi.org\/10.1016\/j.ipm.2018.01.006","journal-title":"Inf Process Manag"},{"issue":"2","key":"4452_CR29","doi-asserted-by":"publisher","first-page":"102,025","DOI":"10.1016\/j.ipm.2019.03.004","volume":"57","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ghorbani AA (2020) An overview of online fake news: characterization, detection, and discussion. Inf Process Manag 57(2):102,025. https:\/\/doi.org\/10.1016\/j.ipm.2019.03.004","journal-title":"Inf Process Manag"},{"key":"4452_CR30","doi-asserted-by":"publisher","unstructured":"de Oliveira Carosia AE, Coelho GP, da Silva AEA (2021) Investment strategies applied to the Brazilian stock market: a methodology based on sentiment analysis with deep learning. Exp Syst Appl 184:115,470. https:\/\/doi.org\/10.1016\/j.eswa.2021.115470","DOI":"10.1016\/j.eswa.2021.115470"},{"issue":"8","key":"4452_CR31","doi-asserted-by":"publisher","first-page":"5584","DOI":"10.1007\/s10489-020-02138-9","volume":"51","author":"M Xie","year":"2021","unstructured":"Xie M, Ye Z, Pan G et al (2021) Incomplete multi-view subspace clustering with adaptive instance-sample mapping and deep feature fusion. Appl Intell 51(8):5584\u20135597. https:\/\/doi.org\/10.1007\/s10489-020-02138-9","journal-title":"Appl Intell"},{"issue":"4","key":"4452_CR32","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1007\/s10462-019-09754-z","volume":"53","author":"IK Nti","year":"2020","unstructured":"Nti IK, Adekoya AF, Weyori BA (2020) A systematic review of fundamental and technical analysis of stock market predictions. Artif Intell Rev 53(4):3007\u20133057. https:\/\/doi.org\/10.1007\/s10462-019-09754-z","journal-title":"Artif Intell Rev"},{"issue":"2","key":"4452_CR33","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s10489-020-01839-5","volume":"51","author":"S Carta","year":"2021","unstructured":"Carta S, Corriga A, Ferreira A et al (2021) A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning. Appl Intell 51(2):889\u2013905. https:\/\/doi.org\/10.1007\/s10489-020-01839-5","journal-title":"Appl Intell"},{"issue":"7","key":"4452_CR34","doi-asserted-by":"publisher","first-page":"3433","DOI":"10.1007\/s12652-020-01839-w","volume":"13","author":"W Khan","year":"2022","unstructured":"Khan W, Ghazanfar MA, Azam MA et al (2022) Stock market prediction using machine learning classifiers and social media, news. J Ambient Intell Humanized Comput 13(7):3433\u20133456. https:\/\/doi.org\/10.1007\/s12652-020-01839-w","journal-title":"J Ambient Intell Humanized Comput"},{"key":"4452_CR35","doi-asserted-by":"publisher","first-page":"101,489","DOI":"10.1109\/ACCESS.2020.2997311","volume":"8","author":"F Rustam","year":"2020","unstructured":"Rustam F, Reshi AA, Mehmood A et al (2020) COVID-19 future forecasting using supervised machine learning models. IEEE Access 8:101,489\u2013101,499. https:\/\/doi.org\/10.1109\/ACCESS.2020.2997311","journal-title":"IEEE Access"},{"issue":"2","key":"4452_CR36","doi-asserted-by":"publisher","first-page":"907","DOI":"10.1007\/s10462-019-09682-y","volume":"53","author":"S Solorio-Fern\u00e1ndez","year":"2020","unstructured":"Solorio-Fern\u00e1ndez S, Carrasco-Ochoa JA, Mart\u00ednez-Trinidad JF (2020) A review of unsupervised feature selection methods. Artif Intell Rev 53(2):907\u2013948. https:\/\/doi.org\/10.1007\/s10462-019-09682-y","journal-title":"Artif Intell Rev"},{"key":"4452_CR37","doi-asserted-by":"publisher","first-page":"61,642","DOI":"10.1109\/ACCESS.2020.2983584","volume":"8","author":"S Garc\u00eda-M\u00e9ndez","year":"2020","unstructured":"Garc\u00eda-M\u00e9ndez S, Fern\u00e1ndez-Gavilanes M, Juncal-Mart\u00ednez J et al (2020) Identifying banking transaction descriptions via support vector machine short-text classification based on a specialized labelled corpus. IEEE Access 8:61,642\u201361,655. https:\/\/doi.org\/10.1109\/ACCESS.2020.2983584","journal-title":"IEEE Access"},{"key":"4452_CR38","doi-asserted-by":"publisher","first-page":"215,679","DOI":"10.1109\/ACCESS.2020.3041084","volume":"8","author":"F De Arriba-P\u00e9rez","year":"2020","unstructured":"De Arriba-P\u00e9rez F, Garc\u00eda-M\u00e9ndez S, Regueiro-Janeiro JA et al (2020) Detection of financial opportunities in micro-blogging data with a stacked classification system. IEEE Access 8:215,679\u2013215,690. https:\/\/doi.org\/10.1109\/ACCESS.2020.3041084","journal-title":"IEEE Access"},{"issue":"1","key":"4452_CR39","first-page":"1","volume":"39","author":"S Beliga","year":"2015","unstructured":"Beliga S, Me\u0161trovi\u0107 A, Martin\u010di\u0107-Ip\u0161i\u0107 S (2015) An overview of graph-based keyword extraction methods and approaches. J Inf Organ Sci 39(1):1\u201320","journal-title":"J Inf Organ Sci"},{"key":"4452_CR40","unstructured":"Kaiser K, Miksch S (2005) Information extraction. A survey. Tech. rep., Institute of Software Technology & Interactive Systems, Vienna University of Technology"},{"key":"4452_CR41","doi-asserted-by":"publisher","unstructured":"Li C, Guo J, Lu Y et al (2018) LDA Meets Word2Vec. In: Proceedings of the The Web Conference. ACM Press, pp 1699\u20131706, DOI https:\/\/doi.org\/10.1145\/3184558.3191629","DOI":"10.1145\/3184558.3191629"},{"issue":"4","key":"4452_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/24751839.2017.1364040","volume":"1","author":"M Azhari","year":"2017","unstructured":"Azhari M, Kumar YJ (2017) Improving text summarization using neuro-fuzzy approach. J Inf Telecommun 1(4):1\u201314. https:\/\/doi.org\/10.1080\/24751839.2017.1364040","journal-title":"J Inf Telecommun"},{"issue":"1","key":"4452_CR43","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s41039-018-0073-0","volume":"13","author":"S Gottipati","year":"2018","unstructured":"Gottipati S, Shankararaman V, Lin JR (2018) Text analytics approach to extract course improvement suggestions from students\u2019 feedback. Res Pract Technol Enhanc Learn 13(1):6. https:\/\/doi.org\/10.1186\/s41039-018-0073-0","journal-title":"Res Pract Technol Enhanc Learn"},{"key":"4452_CR44","unstructured":"L\u00f3pez-\u00dabeda P, D\u00edaz-Galiano MC, Ure\u00f1a-L\u00f3pez LA et al (2021) Pre-trained language models to extract information from radiological reports. In: CEUR Workshop Proceedings, vol 2936. CEUR"},{"issue":"3","key":"4452_CR45","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1016\/j.ijresmar.2019.01.010","volume":"36","author":"SA Vermeer","year":"2019","unstructured":"Vermeer SA, Araujo T, Bernritter SF et al (2019) Seeing the wood for the trees: how machine learning can help firms in identifying relevant electronic word-of-mouth in social media. Int J Res Mark 36 (3):492\u2013508. https:\/\/doi.org\/10.1016\/j.ijresmar.2019.01.010","journal-title":"Int J Res Mark"},{"key":"4452_CR46","doi-asserted-by":"publisher","unstructured":"Jacobs G, Lefever E, Hoste V (2018) Economic event detection in company-specific news text. In: Proceedings of the first workshop on economics and natural language processing. association for computational linguistics, pp 1\u201310, DOI https:\/\/doi.org\/10.18653\/v1\/W18-3101","DOI":"10.18653\/v1\/W18-3101"},{"key":"4452_CR47","doi-asserted-by":"publisher","unstructured":"Oncharoen P, Vateekul P (2018) Deep learning for stock market prediction using event embedding and technical indicators. In: Proceedings of the international conference on advanced informatics: concept theory and applications. IEEE, pp 19\u201324, DOI https:\/\/doi.org\/10.1109\/ICAICTA.2018.8541310","DOI":"10.1109\/ICAICTA.2018.8541310"},{"key":"4452_CR48","doi-asserted-by":"publisher","first-page":"e438","DOI":"10.7717\/peerj-cs.438","volume":"7","author":"S Carta","year":"2021","unstructured":"Carta S, Consoli S, Piras L et al (2021) Event detection in finance using hierarchical clustering algorithms on news and tweets. PeerJ Comput Sci 7:e438. https:\/\/doi.org\/10.7717\/peerj-cs.438","journal-title":"PeerJ Comput Sci"},{"key":"4452_CR49","doi-asserted-by":"publisher","unstructured":"Harb A, Planti\u00e9 M, Dray G et al (2008) Web opinion mining. In: Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology. ACM Press, p 211, DOI https:\/\/doi.org\/10.1145\/1456223.1456269","DOI":"10.1145\/1456223.1456269"},{"key":"4452_CR50","unstructured":"Shilpa B, Shambhavi B (2021) Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments. Kybernetes pp 1\u201326"},{"issue":"2","key":"4452_CR51","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1007\/s00500-019-03982-9","volume":"24","author":"S Gen\u00e7","year":"2020","unstructured":"Gen\u00e7 S, Akay D, Boran FE et al (2020) Linguistic summarization of fuzzy social and economic networks: an application on the international trade network. Soft Comput 24(2):1511\u20131527. https:\/\/doi.org\/10.1007\/s00500-019-03982-9","journal-title":"Soft Comput"},{"issue":"17","key":"4452_CR52","doi-asserted-by":"publisher","first-page":"47","DOI":"10.5120\/ijca2018916301","volume":"179","author":"AY Abu El-Qumsan","year":"2018","unstructured":"Abu El-Qumsan AY, El-Halees AM (2018) Template based medical reports summarization. Int J Comput Appl 179(17):47\u201355. https:\/\/doi.org\/10.5120\/ijca2018916301","journal-title":"Int J Comput Appl"},{"key":"4452_CR53","doi-asserted-by":"publisher","unstructured":"Meena YK, Gopalani D (2020) Statistical features for extractive automatic text summarization. In: Natural language processing: concepts, methodologies, tools, and applications. IGI Global, pp 619\u2013637, DOI https:\/\/doi.org\/10.4018\/978-1-7998-0951-7.ch030","DOI":"10.4018\/978-1-7998-0951-7.ch030"},{"key":"4452_CR54","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.eswa.2018.12.011","volume":"121","author":"S Gupta","year":"2019","unstructured":"Gupta S, Gupta SK (2019) Abstractive summarization: an overview of the state of the art. Exp Syst Appl 121:49\u201365. https:\/\/doi.org\/10.1016\/j.eswa.2018.12.011","journal-title":"Exp Syst Appl"},{"key":"4452_CR55","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1016\/j.jksuci.2020.11.027","volume":"1","author":"M Alhoshan","year":"2020","unstructured":"Alhoshan M, Altwaijry N (2020) AUSS: an Arabic query-based update-summarization system. J King Saud Univ Comput Inf Sci 1:1319\u20131578. https:\/\/doi.org\/10.1016\/j.jksuci.2020.11.027","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"5","key":"4452_CR56","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1016\/j.ipm.2019.02.010","volume":"56","author":"C Barros","year":"2019","unstructured":"Barros C, Lloret E, Saquete E et al (2019) NATSUM: narrative abstractive summarization through cross-document timeline generation. Inf Process Manag 56(5):1775\u20131793. https:\/\/doi.org\/10.1016\/j.ipm.2019.02.010","journal-title":"Inf Process Manag"},{"key":"4452_CR57","doi-asserted-by":"publisher","first-page":"12,008","DOI":"10.1088\/1742-6596\/1453\/1\/012008","volume":"1453","author":"X He","year":"2020","unstructured":"He X, Wang J, Zhang Q et al (2020) Improvement of text segmentation texttiling algorithm. J Phys Conf Ser 1453:12,008\u201312,015. https:\/\/doi.org\/10.1088\/1742-6596\/1453\/1\/012008","journal-title":"J Phys Conf Ser"},{"key":"4452_CR58","doi-asserted-by":"publisher","unstructured":"Clark K, Manning CD (2016) Improving coreference resolution by learning entity-level distributed representations. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers). Association for Computational Linguistics, pp 643\u2013653, DOI https:\/\/doi.org\/10.18653\/v1\/P16-1061","DOI":"10.18653\/v1\/P16-1061"},{"issue":"11","key":"4452_CR59","doi-asserted-by":"publisher","first-page":"15,169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar H, Wang Y, Yuan C et al (2019) Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl 78(11):15,169\u201315,211. https:\/\/doi.org\/10.1007\/s11042-018-6894-4","journal-title":"Multimed Tools Appl"},{"key":"4452_CR60","doi-asserted-by":"publisher","first-page":"104,920","DOI":"10.1016\/j.compbiomed.2021.104920","volume":"138","author":"A Gupta","year":"2021","unstructured":"Gupta A, Katarya R (2021) PAN-LDA: a latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning. Comput Biol Med 138:104,920. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104920","journal-title":"Comput Biol Med"},{"key":"4452_CR61","doi-asserted-by":"publisher","first-page":"116,648","DOI":"10.1016\/j.eswa.2022.116648","volume":"197","author":"S Garc\u00eda-M\u00e9ndez","year":"2022","unstructured":"Garc\u00eda-M\u00e9ndez S, de Arriba-P\u00e9rez F, Barros-Vila A et al (2022) Detection of temporality at discourse level on financial news by combining natural language processing and machine learning. Exp Syst Appl 197:116,648. https:\/\/doi.org\/10.1016\/j.eswa.2022.116648","journal-title":"Exp Syst Appl"},{"key":"4452_CR62","doi-asserted-by":"crossref","unstructured":"Krippendorff K (2018) Content analysis: an introduction to its methodology. SAGE Publications","DOI":"10.4135\/9781071878781"},{"key":"4452_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2017.11.029","volume":"159","author":"JM Sanchez-Gomez","year":"2018","unstructured":"Sanchez-Gomez JM, Vega-Rodr\u00edguez MA, P\u00e9rez CJ (2018) Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach. Knowl-Based Syst 159:1\u20138. https:\/\/doi.org\/10.1016\/j.knosys.2017.11.029","journal-title":"Knowl-Based Syst"},{"key":"4452_CR64","doi-asserted-by":"publisher","first-page":"102,264","DOI":"10.1016\/j.ipm.2020.102264","volume":"57","author":"WS El-Kassas","year":"2020","unstructured":"El-Kassas WS, Salama CR, Rafea AA, et al. (2020) EdgeSumm: graph-based framework for automatic text summarization. Inf Process Manag 57:102,264. https:\/\/doi.org\/10.1016\/j.ipm.2020.102264","journal-title":"Inf Process Manag"},{"key":"4452_CR65","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.eswa.2019.04.028","volume":"131","author":"H Park","year":"2019","unstructured":"Park H, Park T, Lee YS (2019) Partially collapsed Gibbs sampling for latent Dirichlet allocation. Exp Syst Appl 131:208\u2013218. https:\/\/doi.org\/10.1016\/j.eswa.2019.04.028","journal-title":"Exp Syst Appl"},{"issue":"5","key":"4452_CR66","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1002\/ejp.1369","volume":"23","author":"JA Rash","year":"2019","unstructured":"Rash JA, Prkachin KM, Solomon PE et al (2019) Assessing the efficacy of a manual-based intervention for improving the detection of facial pain expression. Eur J Pain 23(5):1006\u20131019. https:\/\/doi.org\/10.1002\/ejp.1369","journal-title":"Eur J Pain"},{"issue":"11","key":"4452_CR67","doi-asserted-by":"publisher","first-page":"1252","DOI":"10.1111\/exd.14022","volume":"28","author":"S Seit\u00e9","year":"2019","unstructured":"Seit\u00e9 S, Khammari A, Benzaquen M et al (2019) Development and accuracy of an artificial intelligence algorithm for acne grading from smartphone photographs. Exp Dermatol 28(11):1252\u20131257. https:\/\/doi.org\/10.1111\/exd.14022","journal-title":"Exp Dermatol"},{"key":"4452_CR68","doi-asserted-by":"publisher","unstructured":"Salminen J, Almerekhi H, Kamel AM et al (2019) Online hate ratings vary by extremes. In: Proceedings of the 2019, Conference on human information interaction and retrieval. Association for Computational Linguistics, pp 213\u2013217, DOI https:\/\/doi.org\/10.1145\/3295750.3298954","DOI":"10.1145\/3295750.3298954"},{"key":"4452_CR69","doi-asserted-by":"publisher","first-page":"103,717","DOI":"10.1016\/j.jbi.2021.103717","volume":"116","author":"H Kilicoglu","year":"2021","unstructured":"Kilicoglu H, Rosemblat G, Hoang L et al (2021) Toward assessing clinical trial publications for reporting transparency. J Biomed Inf 116:103,717\u2013103,727. https:\/\/doi.org\/10.1016\/j.jbi.2021.103717","journal-title":"J Biomed Inf"},{"key":"4452_CR70","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.ijmedinf.2019.05.019","volume":"129","author":"C Gulden","year":"2019","unstructured":"Gulden C, Kirchner M, Sch\u00fcttler C et al (2019) Extractive summarization of clinical trial descriptions. Int J Med Inf 129:114\u2013121. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.05.019","journal-title":"Int J Med Inf"},{"issue":"3","key":"4452_CR71","doi-asserted-by":"publisher","first-page":"102,187","DOI":"10.1016\/j.ipm.2019.102187","volume":"57","author":"C Hark","year":"2020","unstructured":"Hark C, Karc\u0131 A (2020) Karc\u0131 summarization: a simple and effective approach for automatic text summarization using Karc\u0131 entropy. Inf Process Manag 57(3):102,187. https:\/\/doi.org\/10.1016\/j.ipm.2019.102187","journal-title":"Inf Process Manag"},{"key":"4452_CR72","doi-asserted-by":"publisher","first-page":"228,206","DOI":"10.1109\/ACCESS.2020.3046494","volume":"8","author":"R Alqaisi","year":"2020","unstructured":"Alqaisi R, Ghanem W, Qaroush A (2020) Extractive multi-document Arabic text summarization using evolutionary multi-objective optimization with K-Medoid clustering. IEEE Access 8:228,206\u2013228,224. https:\/\/doi.org\/10.1109\/ACCESS.2020.3046494","journal-title":"IEEE Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04452-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04452-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04452-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T05:16:01Z","timestamp":1693199761000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04452-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,10]]},"references-count":72,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["4452"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04452-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,10]]},"assertion":[{"value":"1 January 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2023","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 relevant financial or nonfinancial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent for Publication"}}]}}