Financial Text Mining in Twitterland

S. D. Nikolopoulos (), I. Santouridis and T. Lazaridis
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S. D. Nikolopoulos: TEI Thessaly
I. Santouridis: TEI Thessaly
T. Lazaridis: TEI of Western Macedonia

A chapter in Strategic Innovative Marketing, 2017, pp 105-114 from Springer

Abstract: Abstract We live in a “big” world with “big” information needs and “big” economic data in the form of texts, charts, and numbers. However, although the information set that one may use to analyze a company or to make financial decisions contains both text and numbers, traditionally, theoretical and applied economic research overemphasized the importance of numbers in the decision-making process. In recent years, advances in hardware and software technologies but most importantly the development of advanced text mining and machine learning algorithms has made the efficient utilization of financial text data a reality. In this paper, we review and present several techniques used for financial text analysis and we highlight some potential problems that may arise during the implementation phase of text mining for accounting/financial applications.

Keywords: Financial data mining; Text mining; Text clustering; Sentiment analysis; Topic analysis (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-56288-9_16

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DOI: 10.1007/978-3-319-56288-9_16

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