A MULTIFACETED SENTIMENT ANALYSIS APPROACH TO THE ESTIMATION OF THE STRENGTH OF ONLINE SUPPORT FOR POLITICAL CANDIDATES IN NIGERIA'S ELECTIONS

Online Support Strength of Political Candidates in Nigeria's Elections

Authors

  • Godspower Osaretin Ekuobase Department of Computer Science, University of Benin, Benin City, Edo State, Nigeria
  • Ogheneovo Ajueyitsi Department of Computer Science, University of Benin, Benin City, Edo State, Nigeria

DOI:

https://doi.org/10.33003/fjs-2024-0806-2896

Keywords:

Sentiment analysis, Sentiment analysis model, Text data analytics, Election poll prediction, Nigeria election

Abstract

The strength of online support for political candidates in an election is crucial to their victory at the polls, particularly in countries with advanced digital infrastructure and culture. In modern times, social media is one free space where residents express, and are persuaded to, support or show disdain for political candidates prior to an election. This has resulted in the opinion mining of political tweets to predict electoral victories at the polls. However, this is usually done by adopting a single sentiment analysis model and scraping tool. Ordinarily, no sentiment analysis model or scraping tool is a silver bullet – each has strengths and weaknesses. Thus, this study employed two contemporary scraping tools and adopted three contemporary sentiment analysis models. The models were then exposed to the scrapped political tweets of the top contestants for the Nigeria 2023 presidential election, validated with another set of political tweets of the top contestants for the 2024 Edo State governorship election, and, after that, used to predict the online support strength of the top contestants for the 2024 Ondo State governorship election. Only tweets from within the geopolitical space of elections were scrapped. A notable finding of this study is that no two sentiment analysis models estimate the same online support strength for selected candidates, even with the same set of tweets. Overall, the study holds that online support strength is necessary but insufficient to guarantee victory at the polls in Nigeria's elections.

References

Abouammoh, A. M., & Alshingiti, A. (2009). Statistical and dependability characteristics of the generalized inverted exponential distribution. Communications in Statistics - Theory and Methods, 38(3), 414-426.

Ahmad, I., Mohd, M., & Hasib, M. (2016). Weibull-G family distribution: properties and applications. International Journal of Statistics and Probability, 5(3), 1-14.

Alzaatreh, A., Lee, C., & Famoye, F. (2013). A new method for generating families of continuous distributions. Metron - International Journal of Statistics, 71(1), 63-79.

Alzaghal, M., Kundu, D., & Balakrishnan, N. (2013). T-X factor family of distributions. Journal of Probability and Statistical Science, 11(2), 197-214.

Cordeiro, G. M., Ortega, E. M. & Nadarajah, S. (2010). The Kumaraswamy Weibull distribution with application to failure data.Journal of the Franklin Institute, 347, 8, pp. 1399-1429.

Cordeiro, G. M., Pescim, R. R., Demetrio, C. G. B., Ortega, E. M. M. & Nadarajah, S. (2012).The new class of Kummer beta generalized distributions. Statistics and Operations Research Transactions, 36, pp. 153-180.

Cordeiro, G., & Castro, M. (2011). The Kumaraswamy-G family of distributions. Journal of Data Science, 9(2), 99-113.

Cordeiro, G., & Castro, M. (2015). Type I semi-logistic family of distributions. Journal of Statistical Computation and Simulation, 85(3), 499-513.

Cordeiro, G., Ortega, E., & da Cunha, D. (2014). Semi-logistic family of distributions: properties and applications. Journal of Probability and Statistical Science, 12(1), 37-50.

Dumonceaux, R and Antle, C. (2012). Discrimination between the Log-Normal and the Weibull distributions, Technometrics, 15, 923 – 926. https://doi.org/10.1080/00401706.1973.10489124

Eugene, N., Lee, C., & Famoye, F. (2002). The beta-G family of distributions. Journal of Statistical Analysis and Data Mining, 1(2), 79-95.

Gauss, C. F. (1809). Theoria motvs corporvm coelestivm in sectionibvs conicis Solem ambientivm (in latin). The skew-normal distribution and related multivariate families. Scandinavian Journal of statistics, 32,159-188.

Gupta, R., Kundu, D., & Balakrishnan, N. (1998). The E-G family of distributions. Communications in Statistics - Theory and Methods, 27(8), 1869-1886.

Ibrahim, R., Muhammad, I., & Ahmad, I. (2020). Topp Leone Kumaraswamy-G distribution family: Properties and applications. Journal of Probability and Statistical Science, 18(1), 1-18.

Lizadeh, F., Alizadeh, M., & Nadarajah, S. (2016). The beta Marshall-Olkin family of distributions. Journal of Statistical Distributions Application, 3(1), 1-23.

Marshall, A., & Olkin, I. (1997). The Marshall-Olkin-G family of distributions. Statistical Distributions, 14(3), 167-178.

Ristic, M., & Balakrishnan, N. (2011). Alternative Gamma G distribution: properties and applications. Journal of Probability and Statistical Science, 13(1), 23-37.

Silva, G., Ortega, E., & Cordeiro, G. (2014). Weibull-G family of distributions: properties and applications. Journal of Statistical Computation and Simulation, 84(12), 2688-2707.

Torabi, H., & Montazari, A. (2014). Lomax generator and its application. Journal of Statistical Distributions, 13(4), 167-178.

Zografos, K. & Balakrishnan, N. (2009). On families of beta- and generalized gamma generated distributions and associated inference. Statistical Methodology, 6, pp. 344- 362.

Published

2024-12-31

How to Cite

Ekuobase, G. O., & Ajueyitsi, O. (2024). A MULTIFACETED SENTIMENT ANALYSIS APPROACH TO THE ESTIMATION OF THE STRENGTH OF ONLINE SUPPORT FOR POLITICAL CANDIDATES IN NIGERIA’S ELECTIONS: Online Support Strength of Political Candidates in Nigeria’s Elections. FUDMA JOURNAL OF SCIENCES, 8(6), 184 - 192. https://doi.org/10.33003/fjs-2024-0806-2896