Oladapo Kayode Abiodun

Kayode Abiodun, Oladapo (PhD) is currently a lecturer of Computer Science at McPherson University, Ogun State, Nigeria. He has a Diploma in Computer Data Processing from Ogun State University (2003); a BSc in Computer with Statistics from Olabisi Onabanjo University (2008); a Postgraduate Diploma in Education from Usman Dan Fodio University (2012); an MSc in Business Administration with specialization in Operations Research from University of Lagos (2015); MSc in Information Technology from National Open University (2016) and PhD (2021) from Babcock University, Ilisan-Remo, Ogun State. He also acquired various online certificates to his credit.
Visit here for more details: https://sites.google.com/view/kayodeabiodunoladapo


Session

07-12
13:00
60min
Social Media and Sentimental Analysis: CBN Currency Redesign Policy
Oladapo Kayode Abiodun, Akinbo Racheal Shade

The identification and measurement of an online audience through the social media platform capitalise on the tonality of emotions on the social media presence. On October 20, the most populous country and acclaimed Africa’s largest economy, Nigeria announced the plans to redesign 200, 500 and 1000 banknotes in replacement of the existing ones. Nigerian citizens expressed different opinions over social media in support of or understanding of the proposed plan and process. Research has shown that shared sentiments on social media can influence the opinions of others and thus the Central Bank of Nigeria's currency redesign policy. This study, therefore, aimed to identify and analyse general sentiments towards the process of the currency redesign policy with the purpose of determining the citizen's attitude towards the policy, based on social media comments. Firstly, sentiment analysis was performed on naira redesign-related posts from a selected social media using lexicon-based and supervised machine learning techniques with the purpose of determining a summarised polarity percentage (i.e. negative or positive). The post was collected between January and February 2023. In addition, the performance of the lexicon-based classifier and seven machine learning-based classifiers was implemented and compared in order to use the best-performing classifier in determining the sentiment polarity of the post. Also, the thematic analysis on both positive and negative posts to further understand and revealed general views about the currency redesign policy. Finally, the analytical findings and the possibility of changing the currency redesign policy was discussed.

PyData: Machine Learning, Stats
Main Hall A