Development of a Sentiment Analysis System for Evaluating Student Satisfaction in Ahmadu Bello University, Zaria, Kaduna State
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Background of the Study
Student satisfaction is a key metric that influences academic quality and institutional reputation. At Ahmadu Bello University, Zaria, Kaduna State, traditional methods for gauging student satisfaction—such as paper surveys and informal feedback—are often cumbersome, slow, and subject to human bias. With rapid advancements in natural language processing (NLP) and machine learning, sentiment analysis systems have emerged as promising tools to automatically analyze and interpret large volumes of student feedback from digital platforms (Adebola, 2023). Such systems can process qualitative data from online surveys, social media posts, and course evaluations to extract sentiment scores, thus providing an objective measure of student satisfaction. By leveraging algorithms that detect positive, negative, and neutral sentiments, the system is capable of identifying trends and pinpointing areas that require intervention. The integration of sentiment analysis into the university’s administrative framework can facilitate real-time monitoring of student perceptions and enable data-driven decision-making for curriculum improvements and resource allocation. Furthermore, advanced visualization dashboards can present these insights in an accessible format to academic leaders, promoting transparency and continuous improvement. The system’s scalability allows it to handle increasing volumes of feedback as digital communication grows among students. Despite the promising capabilities of such technology, challenges remain in ensuring the accuracy of sentiment classification, particularly when dealing with sarcasm, regional dialects, and ambiguous expressions. Data privacy and the ethical use of personal opinions also necessitate robust security measures. This study aims to develop a sentiment analysis system tailored for evaluating student satisfaction at Ahmadu Bello University, employing state-of-the-art NLP techniques and machine learning models that continuously learn from new data inputs (Ibrahim, 2024). The research seeks to create a reliable, efficient, and user-friendly tool that not only measures student satisfaction but also offers actionable insights to improve academic services and student support systems (Chinwe, 2025).
Statement of the Problem
At Ahmadu Bello University, existing methods of assessing student satisfaction are hampered by delays, low response rates, and subjectivity inherent in manual data collection. This outdated process results in an incomplete understanding of student needs and often delays critical interventions. The lack of an automated system to capture and analyze the sentiment of student feedback means that potential issues—such as dissatisfaction with course delivery, inadequate support services, or infrastructural deficiencies—may go unnoticed until they have a substantial negative impact on academic outcomes (Olufemi, 2023). Moreover, the reliance on traditional feedback mechanisms prevents real-time analysis and hinders the university’s ability to implement timely, data-driven improvements. In today’s digital age, where students increasingly express opinions through various online channels, the absence of a sophisticated sentiment analysis system leads to missed opportunities for enhancing academic quality and institutional effectiveness. The manual processing of feedback is not only labor-intensive but also prone to errors, thereby affecting the reliability of the results. This study seeks to address these issues by implementing an AI-driven sentiment analysis system capable of processing diverse data sources and providing accurate, real-time evaluations of student satisfaction. Such a system will enable administrators to identify trends and problematic areas promptly, facilitating targeted interventions to improve the overall educational experience and boost student retention.
Objectives of the Study:
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To develop an AI-based sentiment analysis system that processes and classifies student feedback.
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To assess the system’s accuracy and reliability in evaluating student satisfaction.
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To provide actionable recommendations for enhancing academic services based on sentiment insights.
Research Questions:
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How accurately does the sentiment analysis system classify student feedback compared to traditional methods?
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What are the predominant sentiments expressed by students regarding their academic experience?
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How can the insights from the sentiment analysis system inform improvements in university services?
Significance of the Study
This study is significant as it introduces an AI-driven sentiment analysis system to evaluate student satisfaction at Ahmadu Bello University. By automating the assessment of student feedback, the system enhances the timeliness and objectivity of data collection, leading to improved academic planning and resource allocation. The research offers a scalable model that can inform policy and strategic interventions aimed at increasing student satisfaction and retention. These insights are crucial for fostering an environment of continuous improvement and academic excellence (Adebola, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a sentiment analysis system for evaluating student satisfaction at Ahmadu Bello University, Zaria, Kaduna State, and does not extend to other institutions or non-academic feedback.
Definitions of Terms:
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Sentiment Analysis System: An AI-based tool that uses NLP to classify textual data by sentiment.
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Student Satisfaction: The level of contentment expressed by students regarding their academic experience.
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Natural Language Processing (NLP): A branch of AI that focuses on the interaction between computers and human language.
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