Preprint / Version 1

Value Relevance of Social Media Sentiment: An Experiment with Steem using Association Analyses

##article.authors##

  • Sankalp Singh Polygence
  • Debra VanderMeer Florida International University

DOI:

https://doi.org/10.58445/rars.743

Keywords:

Sentiment Analysis, Correlation, Risk Aversion, Social Media, natural language processing

Abstract

Most popular social media platforms support a voting mechanism to capture an assessment of how much the network values a particular post, where more votes or “likes” implies greater valuation among network participants. Such voting mechanisms are subject to confounding factors, such as relative popularity of the poster, as well as outright acts of manipulation to increase vote counts. We hypothesize that post sentiment plays a role in content valuation. We expect that participants will value posts with positive sentiment more than posts with negative sentiment. Further, we postulate that the degree of positive sentiment matters, such posts with a lesser degree of positive sentiment will be more highly valued than posts demonstrating a greater degree of positivity. We base our hypothesis on theories associated with risk aversion, where users are more interested in content that may signal a need to act to avoid potential negative consequences.

References

Arrow, K. J. (1965). "Aspects of the Theory of Risk Bearing". The Theory of Risk Aversion. Helsinki: Yrjo Jahnssonin Saatio. Reprinted in: Essays in the Theory of Risk Bearing, Markham Publ. Co., Chicago, 1971, 90–109.

Bartov, E., Faurel, L. and Mohanram, P.S., (2018). Can Twitter help predict firm-level earnings and stock returns?. Accounting Review 93(3), 25-57.

Chen, H., De, P., Hu, Y.J. and Hwang, B.H., (2014). Wisdom of crowds: The value of stock opinions transmitted through social media. Review of Financial Studies 27(5), 1367-1403.

Chao Li, Balaji Palanisamy, Runhua Xu, Runhua Xu and Jingzhe Wang (2021). "SteemOps: Extracting and Analyzing Key Operations in Steemit Blockchain-based Social Media Platform." Proc. of 11th ACM Conference on Data and Application Security and Privacy (ACM CODASPY'21), Virtual Event, USA,.

Da, Z. and Huang, X., (2020). Harnessing the wisdom of crowds. Management Science 66(5), 1847-1867.

Fang, X., Zhan, J. Sentiment analysis using product review data. (2015) Journal of Big Data 2, 5. https://doi.org/10.1186/s40537-015-0015-2

Jame, R., Johnston, R., Markov, S. and Wolfe, M.,(2016). The value of crowdsourced earnings forecasts. Journal of Accounting Research 54(4), 1077-1110.

Jame, R., Markov, S. and Wolfe, M., (2022). Can fintech competition improve sell-side research quality? Accounting Review 97(4), 287-316.

Pratt, John W. (January 1964). "Risk Aversion in the Small and in the Large". Econometrica. 32 (1/2): 122–136. doi:10.2307/1913738. JSTOR 1913738.

Ren, R., Wu, D. D., and Liu, T. (2019) "Forecasting Stock Market Movement Direction Using Sentiment Analysis and Support Vector Machine," IEEE Systems Journal, 13(1), 760-770, doi: 10.1109/JSYST.2018.2794462.

Smailović, J., Grčar, M., Lavrač, N., Žnidaršič, M. (2013). Predictive Sentiment Analysis of Tweets: A Stock Market Application. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_8

Valdivia, A., Hrabova, E., Chaturvedi, I., Luzón, M. V., Troiano, L., Cambria, E., Herrera, F.,

(2019). Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods. Neurocomputing 353, 3-16. https://doi.org/10.1016/j.neucom.2018.09.096.

Zvarevashe, K. and Olugbara O. O., (2018) A framework for sentiment analysis with opinion mining of hotel reviews, 2018 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 2018, pp. 1-4, doi: 10.1109/ICTAS.2018.8368746.

VADER: https://github.com/cjhutto/vaderSentiment

Google: https://cloud.google.com/natural-language

IBM Watson: https://www.ibm.com/cloud/watson-natural-language-understanding

TextBlob: https://textblob.readthedocs.io/en/dev/index.html

Flair: https://github.com/flairNLP/flair

Downloads

Posted

2023-11-29