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Financial Sentiment Analysis on Twitter During Covid-19 Pandemic in the UK

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Advances on Intelligent Computing and Data Science (ICACIn 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 179))

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Abstract

The surge in Covid-19 cases seen in 2020 has caused the UK government to enact regulations to stop the virus’s spread. Along with other aspects like altered customer confidence and activity, the financial effects of these actions must be taken into account. This later can be studied from the user generated content posted on social networks such as Twitter. In this paper, we provide a supervised technique to analyze tweets exhibiting bullish and bearish sentiments, by predicting a sentiment class positive, negative, or neutral. Both machine learning & deep learning techniques are implemented to predict our financial sentiment class. Our research highlights how word embeddings, most importantly word2vec may be effectively used to conduct sentiment analysis in the financial sector providing favorable solutions. In addition, comprehensive research has been elicited between our technique and a lexicon-based approach. The outcomes of the study indicate how well Word2Vec model with deep learning techniques outperforms the others with an accuracy of 87%.

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Notes

  1. 1.

    https://github.com/lordmayor231/UPDATED-Financial-sentiment-analysis-during-Covid19-in-the-UK.git.

References

  1. Sudhir, P., Suresh, V.D.: Comparative study of various approaches, applications and classifiers for sentiment analysis. Glob. Transitions Proc. 2(2), 205–211 (2021)

    Article  Google Scholar 

  2. Costola, M., Iacopini, M., Santagiustina, C.R.M.A.: Google search volumes and the financial markets during the COVID-19 outbreak. Finan. Res. Lett. 42, 101884 (2021). https://doi.org/10.1016/j.frl.2020.101884

    Article  Google Scholar 

  3. Keogh-Brown, M.R., Jensen, H.T., Edmunds, W., Smith, R.D.: The impact of covid-19, associated behaviours and policies on the UK economy: a computable general equilibrium model. SSM – Popul. Health 12, 100651 (2020)

    Article  Google Scholar 

  4. Presti, G., Mchugh, L., Gloster, A., Karekla, M., Hayes, S.: The dynamics of fear at the time of covid-19: a contextual behavioral science perspective. Clin. Neuropsychiatry 17, 65–71 (2020)

    Google Scholar 

  5. Basiri, M.E., Nemati, S., Abdar, M., Asadi, S., Acharrya, U.R.: A novel fusion-based deep learning model for sentiment analysis of covid-19 tweets. Knowl.-Based Syst. 228, 107242 (2021). https://doi.org/10.1016/j.knosys.2021.107242

    Article  Google Scholar 

  6. Hota, H.S., Sharma, D.K., Verma, N.: Lexicon-based sentiment analysis using twitter data: a case of covid-19 outbreak in India and abroad. In: Data Science for COVID-19, pp. 275–295. Elsevier (2021)

    Google Scholar 

  7. Huang, A.H., Zang, A.Y., Zheng, R.: Evidence on the information content of text in analyst reports. Accounting Review 89(6), 2151–2180 (2014)

    Article  Google Scholar 

  8. Antweiler, W., Frank, M.Z.: Is all that talk just noise? The information content of internet stock message boards. J. Finance 59(3), 1259–1294 (2004). https://doi.org/10.1111/j.1540-6261.2004.00662.x

    Article  Google Scholar 

  9. Barber, B.M., Odean, T., Zheng, L.: Out of sight, out of mind: The effects of expenses on mutual fund flows. J. Bus. 78(6), 2095–2120 (2005)

    Article  Google Scholar 

  10. Jiang, F., Lee, J., Martin, X., Zhou, G.: Manager sentiment and stock returns. J. Financ. Econ. 132(1), 126–149 (2019)

    Article  Google Scholar 

  11. Kwon, K.T., Ko, J.-H., Shin, H., Sung, M., Kim, J.Y.: Drive-through screening center for COVID-19: a safe and efficient screening system against massive community outbreak. J. Korean Med. Sci. 35(11), e123 (2020). https://doi.org/10.3346/jkms.2020.35.e123

    Article  Google Scholar 

  12. Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Coling 2010: Posters, pp. 36–44 (2010)

    Google Scholar 

  13. Baker, S.R., Bloom, N., Davis, S.J., Kost, K., Sammon, M., Viratyosin, T.: The unprecedented stock market reaction to covid-19. Rev. Asset Pricing Stud. 10(4), 742–758 (2020). https://doi.org/10.1093/rapstu/raaa008

    Article  Google Scholar 

  14. Eachempati, P., Srivastava, P.R., Panigrahi, P.K.: Sentiment analysis of covid-19 pandemic on the stock market. Am. Bus. Rev. 24(1), 141–165 (2021). https://doi.org/10.37625/abr.24.1.141-165

    Article  Google Scholar 

  15. Biswas, S., Sarkar, I., Das, P., Bose, R., Roy, S.: Examining the effects of pandemics on stock market trends through sentiment analysis. Xi’an Dianzi Keji Daxue Xuebao/Journal of Xidian University 14, 1–14 (2020)

    Google Scholar 

  16. Li, S., Wang, Y., Xue, J., Zhao, N., Zhu, T.: The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. Int. J. Environ. Res. Public Health 17(6), 2032 (2020). https://doi.org/10.3390/ijerph17062032

    Article  Google Scholar 

  17. Chakraborty, K., et al.: Sentiment analysis of covid-19 tweets by deep learning classifiers—a study to show how popularity is affecting accuracy in social media. Appl. Soft Comput. 97, 106754 (2020)

    Article  Google Scholar 

  18. Ghasiya, P., Okamura, K.: Investigating covid-19 news across four nations: a topic modeling and sentiment analysis approach. IEEE Access 9, 36645–36656 (2021)

    Article  Google Scholar 

  19. Barkur, G., Vibha, Kamath, G.B.: Sentiment analysis of nationwide lockdown due to covid 19 outbreak: evidence from India. Asian J. Psychiatry 51, 102089 (2020). https://doi.org/10.1016/j.ajp.2020.102089

    Article  Google Scholar 

  20. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of Workshop at ICLR, 2013 (2013)

    Google Scholar 

  21. Sithole, V.: Fine-tuning semantic information for optimized classification of the internet of things patterns using neural word embeddings. J. Adv. Comput. Netw. 8(1), 26–30 (2020)

    Article  Google Scholar 

  22. Mikolov, T., Chen, K., Corrado, G., Dean, J.:. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  23. Muhammad, P.F., Kusumaningrum, R., Wibowo, A.: Sentiment analysis using Word2vec and long short-term memory (LSTM) for Indonesian hotel reviews. Procedia Comput. Sci. 179, 728–735 (2021). https://doi.org/10.1016/j.procs.2021.01.061

    Article  Google Scholar 

  24. Jing, L.-P., Huang, H.-K., Shi, H.-B.: Improved feature selection approach tfidf in text mining. In: Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 2, pp. 944–946 (2002)

    Google Scholar 

  25. Morin, F., Bengio, Y.: Hierarchical probabilistic neural network language model. In: Robert, G.C., Ghahramani, Z. (eds.) Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, volume R5 of Proceedings of Machine Learning Research, pp. 246–252. PMLR, 06–08 Jan 2005. Reissued by PMLR on 30 March 2021

    Google Scholar 

  26. Etaiwi, W., Suleiman, D., Awajan, A.: Deep learning based techniques for sentiment analysis: a survey. Informatica 45(7) (2021)

    Google Scholar 

  27. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  28. Agrawal, A.K., Chakraborty, G.: On the use of acquisition function-based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection. Struct. Control. Health Monit. 28(4), e2693 (2021)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK

    Oluwamayowa Ashimi, Amna Dridi & Edlira Vakaj

Authors
  1. Oluwamayowa Ashimi
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  2. Amna Dridi
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  3. Edlira Vakaj
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Corresponding author

Correspondence to Oluwamayowa Ashimi .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Information Systems Department, School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  3. Faculty of Sciences, University of Hassan II Casablanca, Casablanca, Morocco

    Errais Mohammed

  4. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  5. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

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Ashimi, O., Dridi, A., Vakaj, E. (2023). Financial Sentiment Analysis on Twitter During Covid-19 Pandemic in the UK. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_33

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