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All Journal Andalasian International Journal of Applied Science, Engineering, and Technology Journal of Applied Artificial Intelligence in Education
Lasisi, Mariam Adenike
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Mechanical Engineering

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Deep Learning-Based Sentiment Analysis Using Gated Recurrent Unit Adelakun, Najeem Olawale; Lasisi, Mariam Adenike
Andalasian International Journal of Applied Science, Engineering and Technology Vol. 5 No. 1 (2025): March 2025
Publisher : LPPM Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/aijaset.v5i1.217

Abstract

In today’s volatile financial markets, investor sentiment plays a crucial role in shaping market dynamics and influencing investment decisions. Traditional analytical methods often fail to capture the subtle emotional cues embedded in vast amounts of unstructured textual data derived from news articles, social media, and financial reports. This study addresses this challenge by employing a deep learning-based approach using Gated Recurrent Units (GRU) for sentiment analysis, thereby enhancing the accuracy of financial market predictions. The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. Sentiment labeling and feature engineering, utilizing word embeddings, convert textual data into a format suitable for deep learning. The Gated Recurrent Unit (GRU) model is then trained on these features, and its performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that while the Gated Recurrent Unit (GRU) model effectively captures neutral sentiments, it struggles to accurately classify negative and positive sentiments, highlighting areas for improvement. These findings underscore the potential of GRU-based models in financial sentiment analysis while emphasizing the need for refined techniques to enhance classification accuracy. Future research should investigate hybrid architectures, integrate attention mechanisms, and leverage real-time data to enhance the robustness and comprehensiveness of market forecasting. These insights strongly advocate for ongoing advancements in deep learning strategies to refine sentiment classification and financial prediction models.
System Based Artificial Intelligence Adoption in Nigerian Tertiary Education: A Conceptual Governance Model for Learning, Academic Integrity, and Equity Adelakun, Najeem Olawale; Lasisi, Mariam Adenike; Kolawole, Tolani Damilola
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.713

Abstract

This study explores the adoption of Artificial Intelligence in Nigerian tertiary education institutions from a conceptual governance perspective, specifically on learning outcomes, academic integrity and equity in HEIs. AI is no longer a tool for learning support but is a governance issue that requires policies and guidelines, ethical supervision, and pedagogical incorporation as it becomes more central to academic life. The study employs the narrative review method to integrate the empirical and conceptual evidence and to gain insight into the role of different types of Artificial Intelligence systems and the shifts in student learning practices and responses to the changes in institutional policy (2019-2025). The analysis presents Artificial Intelligence as an ecosystem of technologies and not as a single technology, and outlines the different governance considerations for each system type. The analysis reveals that Artificial Intelligence plays a role in the areas of personalised learning, academic productivity and access to educational resources in tertiary institutions in Nigeria. The benefits have many drawbacks, including academic dishonesty in generative systems, privacy and transparency issues in adaptive systems, and equity and dependency issues in intelligent tutoring systems. The institutional governance has been a gradual process and the students have been slow to take up Artificial Intelligence tools. The paper concludes that the integration of Artificial Intelligence in tertiary education in Nigeria should be structured, supported by a framework for policy development and Artificial Intelligence literacy, curriculum reform, and investment in digital infrastructure should have implications for other similar higher education systems in Sub Saharan Africa.
Co-Authors Adelakun, Najeem Olawale Kolawole, Tolani Damilola