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All Journal Kwaghe International Journal of Engineering and Information Technology
Ashraf Ishaq
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering

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Tiktok Through AI Eyes: A Deep Learning Approach to Sentiment Analysis Hambali Moshood Abiola; Ayo Iyanuoluwa; Akinyemi Adesina A.; Adamu Muhammed Gadafi; Ashraf Ishaq
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i2.5485

Abstract

Background: The rapid growth of social media has transformed communication, with TikTok standing out among younger users for its short-form videos. Understanding user sentiment on these platforms is key to analyzing public opinion, trends, and engagement. Aim: This study explores sentiment analysis of TikTok user reviews using deep learning approaches, specifically Recurrent Neural Networks with Long Short-Term Memory (RNN-LSTM) and Deep Belief Networks (DBN). With over 144,000 reviews collected from Google Play and Apple App stores, the dataset was preprocessed using techniques such as lemmatization, tokenization, and GloVe word embeddings. The reviews were then classified into positive and negative sentiments. Both models were trained and evaluated based on metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Result: Experimental results revealed that the RNN-LSTM model outperformed the DBN, achieving an accuracy of 81.99% and an AUC of 0.8874, compared to DBN's 78.53% accuracy and 0.8577 AUC. The findings demonstrate the effectiveness of deep learning—particularly LSTM—in capturing sentiment from noisy, user-generated content on platforms like TikTok. This work contributes to the growing field of AI-driven sentiment analysis and provides a foundation for future improvements through hybrid or multimodal approaches.
The Role of Blockchain in Securing IoT Devices Abubakar Jibrin; Ashraf Ishaq; Aliyu Ahmed; Adamu Muhammad Gadafi
Kwaghe International Journal of Engineering and Information Technology Vol 2 No 2 (2025): Kwaghe International Journal of Engineering and Information Technology
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/kijeit.v2i2.5584

Abstract

The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, including data breaches, unauthorized access, and the exploitation of centralized network vulnerabilities. Traditional security architectures struggle to provide robust protection due to the distributed and resource-constrained nature of IoT environments. Blockchain technology, with its decentralized ledger, cryptographic security, and smart contract functionality, presents a promising approach to mitigating these risks. By ensuring data integrity, enabling secure authentication, and facilitating trustless interactions among IoT devices, blockchain can enhance the overall security framework of IoT ecosystems. This paper critically examines the role of blockchain in securing IoT networks, outlining its key benefits, potential real-world applications, and associated limitations. While blockchain addresses fundamental IoT security concerns, challenges such as scalability, computational overhead, and integration complexity hinder widespread adoption. The study underscores the need for further research into optimizing blockchain protocols for IoT environments and explores potential advancements in hybrid security models.
Co-Authors Abubakar Jibrin Adamu Muhammad Gadafi Adamu Muhammed Gadafi Akinyemi Adesina A. Aliyu Ahmed Ayo Iyanuoluwa Hambali Moshood Abiola