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Intelligent Methods and Big Data in Industrial Applications pp 367–374Cite as

Context Sensitive Sentiment Analysis of Financial Tweets: A New Dictionary

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Part of the book series: Studies in Big Data ((SBD,volume 40))

Abstract

Sentiment analysis can make a contribution to behavioral economics and behavioral finance. It is concerned with the effect of opinions and emotions on economical or financial decisions. In sentiment analysis, or in opinion mining as they often call it, emotions or opinions of various degrees are assigned to the text (tweets in this case) under consideration. This paper describes an application of a lexicon-based domain-specific approach to a set of tweets in order to calculate sentiment analysis of the tweets. Further, we introduce a domain-specific lexicon for the financial domain and compare the results with those reported in other studies. The results show that using a context-sensitive set of positive and negative words, rather than one that includes general keywords, produces better outcomes than those achieved by humans on the same set of tweets.

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Author information

Authors and Affiliations

  1. UNC Charlotte, Charlotte, USA

    Narges Tabari & Mirsad Hadzikadic

Authors
  1. Narges Tabari
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  2. Mirsad Hadzikadic
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Corresponding author

Correspondence to Narges Tabari .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Robert Bembenik

  2. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Łukasz Skonieczny

  3. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Grzegorz Protaziuk

  4. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  5. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

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Tabari, N., Hadzikadic, M. (2019). Context Sensitive Sentiment Analysis of Financial Tweets: A New Dictionary. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_26

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