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The\n            <jats:italic>first stream<\/jats:italic>\n            focuses on defining tasks and developing techniques for FSA, and its main objective is to improve the performances of various FSA tasks by advancing methods and using\/curating human-annotated datasets. The\n            <jats:italic>second stream<\/jats:italic>\n            of research focuses on using financial sentiment, implicitly or explicitly, for downstream applications on financial markets, which has received more research efforts. The main objective is to discover appropriate market applications for existing techniques. More specifically, the application of FSA mainly includes hypothesis testing and predictive modeling in financial markets. This survey conducts a comprehensive review of FSA research in both the technique and application areas and proposes several frameworks to help understand the two areas\u2019 interactive relationship. This article defines a clearer scope for FSA studies and conceptualizes the FSA-investor sentiment-market sentiment relationship. Major findings, challenges, and future research directions for both FSA techniques and applications have also been summarized and discussed.\n          <\/jats:p>","DOI":"10.1145\/3649451","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T12:35:45Z","timestamp":1708950945000},"page":"1-42","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":121,"title":["Financial Sentiment Analysis: Techniques and Applications"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7856-3140","authenticated-orcid":false,"given":"Kelvin","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-3937","authenticated-orcid":false,"given":"Frank","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1082-8755","authenticated-orcid":false,"given":"Rui","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-1280","authenticated-orcid":false,"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1057"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.najef.2019.03.019"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1080\/1331677X.2018.1559748"},{"key":"e_1_3_3_5_2","article-title":"FinBERT: Financial sentiment analysis with pre-trained language models","author":"Araci Dogu","year":"2019","unstructured":"Dogu Araci. 2019. 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