{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:07:44Z","timestamp":1783613264755,"version":"3.55.0"},"reference-count":8,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T00:00:00Z","timestamp":1677024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Tokyo University of Science"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Life Robotics"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years, unspecified messages posted on social media have significantly affected the price fluctuations of online-traded products, such as stocks and virtual currencies. In this study, we investigate whether information on Twitter and natural language expressions in tweets can be used as features for predicting market information, such as price changes in virtual currencies and sudden price changes. Our method is based on features created using Sentence-BERT for tweet data. These features were used to train the light-gradient boosting machine (LightGBM), a variant of the gradient boosting ensemble framework that uses tree-based machine learning models, with the target variable being a sudden change in closing price (sudden drop, sudden rise, or no sudden change). We set up a classification task with three labels using the features created by the proposed method for prediction. We compared the prediction results with and without these new features and discussed the advantages of linguistic features for predicting changes in cryptocurrency trends.<\/jats:p>","DOI":"10.1007\/s10015-023-00857-z","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T17:03:06Z","timestamp":1677085386000},"page":"410-417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Market prediction using machine learning based on social media specific features"],"prefix":"10.1007","volume":"28","author":[{"given":"Satoshi","family":"Sekioka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryo","family":"Hatano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Nishiyama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"857_CR1","doi-asserted-by":"crossref","unstructured":"Reimers Nils, Gurevych Iryna (2019) Sentence-Bert: Sentence embeddings using SIAMESE Bert-networks. arXiv preprint arXiv:1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"key":"857_CR2","unstructured":"Devlin Jacob, Chang Ming-Wei, Lee Kenton, Toutanova Kristina (2018) BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"857_CR3","first-page":"3146","volume":"30","author":"Guolin Ke","year":"2017","unstructured":"Ke Guolin, Meng Qi, Finley Thomas, Wang Taifeng, Chen Wei, Ma Weidong, Ye Qiwei, Liu Tie-Yan (2017) LightGBM: A highly efficient gradient boosting decision tree. Adv Neural Info Process Syst 30:3146\u20133154","journal-title":"Adv Neural Info Process Syst"},{"key":"857_CR4","doi-asserted-by":"crossref","unstructured":"Caron Matthew, Muller Oliver (2020) Hardening soft information: A transformer-based approach to forecasting stock return volatility. In 2020 IEEE International Conference on Big Data (Big Data), pages 4383\u20134391. IEEE","DOI":"10.1109\/BigData50022.2020.9378134"},{"key":"857_CR5","first-page":"189","volume-title":"International Workshop on Explainable","author":"Ohana Jean Jacques","year":"2021","unstructured":"Jacques Ohana Jean, Steve Ohana, Eric Benhamou, David Saltiel, Beatrice Guez (2021) Explainable AI (XAI) models applied to the multi-agent environment of financial markets. International Workshop on Explainable. Transparent Autonomous Agents and Multi-Agent Systems, Springer, Berlin, pp 189\u2013207"},{"key":"857_CR6","unstructured":"Lundberg Scott\u00a0M, Lee Su-In (2017) A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems, pages 4768\u20134777"},{"key":"857_CR7","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.476","volume":"7","author":"Pooja Mehta","year":"2021","unstructured":"Mehta Pooja, Pandya Sharnil, Kotecha Ketan (2021) Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science 7:e476","journal-title":"PeerJ Computer Science"},{"key":"857_CR8","unstructured":"Microsoft (2017) Light GBM[Source code]. https:\/\/github.com\/microsoft\/LightGBM"}],"container-title":["Artificial Life and Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-023-00857-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10015-023-00857-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-023-00857-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T17:07:56Z","timestamp":1682528876000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10015-023-00857-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,22]]},"references-count":8,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["857"],"URL":"https:\/\/doi.org\/10.1007\/s10015-023-00857-z","relation":{},"ISSN":["1433-5298","1614-7456"],"issn-type":[{"value":"1433-5298","type":"print"},{"value":"1614-7456","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,22]]},"assertion":[{"value":"15 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}