IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v10y2023i11p481-491.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-10-issue-11/481-491.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/analyzing-public-sentiment-and-acceptance-of-the-bimodal-voter-accreditation-system-in-nigeria-using-sentiment-analysis-and-roberta-model/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uttara Ananthakrishnan & Davide Proserpio & Siddhartha Sharma, 2023. "I Hear You: Does Quality Improve with Customer Voice?," Marketing Science, INFORMS, vol. 42(6), pages 1143-1161, November.
    2. Miriam Venturini, 2023. "The Imperfect Union: Labor Racketeering, Corruption Exposure, and Its Consequences," Working Papers 202407, University of California at Riverside, Department of Economics.
    3. de Haan, Evert & Padigar, Manjunath & El Kihal, Siham & Kübler, Raoul & Wieringa, Jaap E., 2024. "Unstructured data research in business: Toward a structured approach," Journal of Business Research, Elsevier, vol. 177(C).
    4. Li Xian Liu & Zhiyue Sun & Kunpeng Xu & Chao Chen, 2024. "AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges," IJFS, MDPI, vol. 12(3), pages 1-35, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:10:y:2023:i:11:p:481-491. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.