Skip to main content

Towards Financial Sentiment Analysis in a South African Landscape

  • Conference paper
  • First Online:
Machine Learning and Knowledge Extraction (CD-MAKE 2021)


Sentiment analysis as a sub-field of natural language processing has received increased attention in the past decade enabling organisations to more effectively manage their reputation through online media monitoring. Many drivers impact reputation, however, this thesis focuses only the aspect of financial performance and explores the gap with regards to financial sentiment analysis in a South African context. Results showed that pre-trained sentiment analysers are least effective for this task and that traditional lexicon-based and machine learning approaches are best suited to predict financial sentiment of news articles. The evaluated methods produced accuracies of 84%–94%. The predicted sentiments correlated quite well with share price and highlighted the potential use of sentiment as an indicator of financial performance. A main contribution of the study was updating an existing sentiment dictionary for financial sentiment analysis. Model generalisation was less acceptable due to the limited amount of training data used. Future work includes expanding the data set to improve general usability and contribute to an open-source financial sentiment analyser for South African data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

  2. 2.

  3. 3.

    Valence Aware Dictionary for sEntiment Reasoning.

  4. 4.

  5. 5.

  6. 6.

  7. 7.

  8. 8.

  9. 9.

  10. 10.

  11. 11.

  12. 12.

  13. 13.


  1. Chowdhury, S.G., Routh, S., Chakrabarti, S.: News analytics and sentiment analysis to predict stock price trends. Int. J. Comput. Sci. Inf. Technol. 5(3), 3595–3604 (2014)

    Google Scholar 

  2. Dang, N.C., Moreno-García, M.N., De la Prieta, F.: Sentiment analysis based on deep learning: a comparative study. Electronics (Switzerland) 9(3) (2020).

  3. Hajek, P., Olej, V., Myskova, R.: Forecasting corporate financial performance using sentiment in annual reports for stakeholders’ decision-making. Technol. Econ. Dev. Econ. 20(4), 721–738 (2014).

    Article  Google Scholar 

  4. Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. 30(4), 330–338 (2018).

  5. Hutto, C., Gilbert, E.: VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, pp. 216–225 (2014)

    Google Scholar 

  6. Jiang, M., Lan, M., Wu, Y.: ECNU at SemEval-2017 task 5: an ensemble of regression algorithms with effective features for fine-grained sentiment analysis in financial domain. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pp. 888–893 (2017).

  7. Joshi, K., Bharathi , H.N., Rao, J.: Stock trend prediction using news sentiment analysis. Int. J. Comput. Sci. Inf. Technol. 8(3), 67–76 (2016).

  8. Kumar, A., Sethi, A., Akhtar, S., Ekbal, A., Biemann, C., Bhattacharyya, P.: IITPBat SemEval-2017 task 5: sentiment prediction in financial text. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pp. 894–898 (2017).

  9. Kumaresh, N., Bonta, V., Janardhan, N.: A Comprehensive study on lexicon based approaches for sentiment analysis. Asian J. Comput. Sci. Technol. 8(S2), 1–6 (2019). www.rottentomatoes

  10. Landis, R.J., Koch, G.G.: The measurement of observer agreement for categorical data. In: Biometrics, pp. 159–174 (1977)

    Google Scholar 

  11. Lappeman, J., Clark, R., Evans, J., Sierra-Rubia, L., Gordon, P.: Studying social media sentiment using human validated analysis. MethodsX 7, 100867 (2020).

  12. Lei, Q.: Financial value of reputation: evidence from the ebay auctions of gmail invitations. J. Ind. Econ. 59(3), 422–456 (2011)

    Article  Google Scholar 

  13. Loughran, T., McDonald, B.: When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Financ. 66(1), 35–65 (2011)

    Article  Google Scholar 

  14. Mansar, Y., Gatti, L., Ferradans, S., Guerini, M., Staiano, J.: Fortia-FBK at SemEval-2017 task 5: bullish or bearish? Inferring sentiment towards brands from financial news headlines. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pp. 817–822 (2017).

  15. Mäntylä, M.V., Graziotin, D., Kuutila, M.: The evolution of sentiment analysis–a review of research topics, venues, and top cited papers. Comput. Sci. Rev. 27(February), 16–32 (2018).

    Article  Google Scholar 

  16. Mishev, K., Gjorgjevikj, A., Vodenska, I., Chitkushev, L.T., Trajanov, D.: Evaluation of sentiment analysis in finance: from lexicons to transformers. IEEE Access 8, 131662–131682 (2020)

    Article  Google Scholar 

  17. Moore, A., Rayson, P.: Lancaster A at SemEval-2017 task 5: evaluation metrics matter: predicting sentiment from financial news headlines. In: Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pp. 581–585 (2017).

  18. Mudinas, A., Zhang, D., Levene, M.: Market trend prediction using sentiment analysis: lessons learned and paths forward (2019). arXiv:1903.05440

  19. Odendaal, H., Johannes, N., Reid, M.: Media based sentiment indices as an alternative measure of consumer confidence (2018). A Working paper of the Department of Economics and the Bureau for Economic Research at the University of Stellenbosch. Accessed 13 Mar 2020

  20. Petz, G., Karpowicz, M., Fürschuß, H., Auinger, A., Stříteskỳ, V., Holzinger, A.: Reprint of: computational approaches for mining user’s opinions on the web 2.0. Inf. Process. Manag. 51(4), 510–519 (2015)

    Google Scholar 

  21. Saberi, B., Saad, S.: Sentiment analysis or opinion mining: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 7(5), 1660–1666 (2017).

  22. Terblanche, M., Marivate, V.: LM-SA-2020 sentiment word list, April 2021.

  23. Vig, S., Dumicic, K., Klopotan, I.: The impact of reputation on corporate financial performance: median regression approach. Bus. Syst. Res. 8(2), 40–58 (2017).

    Article  Google Scholar 

  24. Zimmerman, V.: A new way to sentiment-tag financial news (2019). Accessed 13 Feb 2020

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Vukosi Marivate .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Terblanche, M., Marivate, V. (2021). Towards Financial Sentiment Analysis in a South African Landscape. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2021. Lecture Notes in Computer Science(), vol 12844. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84059-4

  • Online ISBN: 978-3-030-84060-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics