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Analyzing Public Sentiment and Acceptance of the Bimodal Voter Accreditation System in Nigeria using Sentiment Analysis and RoBERTa Model

Author

Listed:
  • Idi Mohammed

    (Department of Computer Science, Yobe State University, Damaturu. Nigeria)

  • Zanna Bulama

    (Department of Computer Science, College of Agriculture, Science, and Technology, Gujba Nigeria)

Abstract

The research article investigates the satisfaction of voters with the deployment of the Bimodal Voter Accreditation System (BVAS) in the 2023 Nigerian presidential election by conducting a sentiment analysis of Twitter posts. The article aims to analyze public opinion and sentiment towards the BVAS system and provide valuable insights that can inform policy decisions and actions related to the implementation and improvement of the system. In this study, Twitter posts related to BVAS and the election were collected using the Twitter streaming API. A total of 997,400 tweets were obtained. The data preprocessing phase involved cleaning the tweets by removing noise, filtering out irrelevant comments, and segmenting the text into words or phrases. The sentiment analysis itself was conducted using the Robustly Optimized BERT Pretraining Approach (ROBERTa), which is a language model introduced by Facebook AI. ROBERTa is a powerful model for sequence modeling and is optimized for sentiment analysis. It predicts the sentiment of each tweet as either positive or negative. This study reveals that attitudes toward the employment of BVAS in the Nigerian presidential election of 2023 are neutral, the acceptance rate is low compared to the rejection rate. Although, there is a large percentage of people who are unfavorable to the introduction of this technology. The study utilized Twitter data exclusively for sentiment analysis, which may limit its accuracy in representing the sentiments of the entire population. Twitter users may not be a fully representative sample and could introduce biases. Additionally, the sentiment analysis focused on English tweets only, potentially overlooking sentiments expressed in other languages spoken in Nigeria, like Hausa, Yoruba, or Igbo. To achieve a more comprehensive analysis, future research could expand to include sentiment analysis of tweets in different languages.

Suggested Citation

  • Idi Mohammed & Zanna Bulama, 2023. "Analyzing Public Sentiment and Acceptance of the Bimodal Voter Accreditation System in Nigeria using Sentiment Analysis and RoBERTa Model," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(11), pages 481-491, November.
    • Handle: RePEc:bjc:journl:v:10:y:2023:i:11:p:481-491
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    References listed on IDEAS

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    1. Hartmann, Jochen & Heitmann, Mark & Siebert, Christian & Schamp, Christina, 2023. "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 75-87.
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