Exploring the Role of AI-Based Sentiment Analysis in Measuring Student Satisfaction: A Case Study of Federal University, Lokoja (Lokoja LGA, Kogi State)
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- NGN 5000
Background of the Study
Student satisfaction is a critical indicator of the quality of education and services provided by universities. Traditionally, measuring student satisfaction involves surveys and feedback forms, which can be time-consuming and may not always provide accurate insights. AI-based sentiment analysis offers an opportunity to analyze large volumes of student feedback and social media data to gain a deeper understanding of student sentiments. This study explores the application of AI-powered sentiment analysis tools to measure student satisfaction at Federal University, Lokoja, located in Lokoja LGA, Kogi State.
Statement of the Problem
Traditional methods of measuring student satisfaction can be limited in scope and effectiveness. AI-based sentiment analysis offers a more efficient and comprehensive approach to understanding student sentiments. However, the use of sentiment analysis for this purpose in Nigerian universities, such as Federal University, Lokoja, has not been thoroughly investigated.
Objectives of the Study
1. To explore the role of AI-based sentiment analysis in measuring student satisfaction at Federal University, Lokoja.
2. To assess the accuracy and effectiveness of sentiment analysis tools in extracting meaningful insights from student feedback.
3. To identify the potential challenges and benefits of using AI for student satisfaction measurement.
Research Questions
1. How can AI-based sentiment analysis improve the measurement of student satisfaction at Federal University, Lokoja?
2. How accurate are AI-powered sentiment analysis tools in analyzing student feedback from surveys and online platforms?
3. What challenges and benefits are associated with using sentiment analysis to measure student satisfaction?
Research Hypotheses
1. AI-based sentiment analysis will provide more accurate and timely insights into student satisfaction than traditional feedback methods.
2. Students' sentiments, as analyzed through AI, will correlate strongly with their overall satisfaction and engagement with university services.
3. Challenges in implementing AI-based sentiment analysis will include data privacy concerns, inaccuracies in sentiment interpretation, and technical infrastructure issues.
Significance of the Study
This study will provide universities with a new approach to understanding student satisfaction through the use of AI-based sentiment analysis. The findings will help university administrators make more informed decisions regarding policies and improvements to student services and engagement.
Scope and Limitations of the Study
The study will focus on the application of sentiment analysis tools to student feedback at Federal University, Lokoja. Limitations include the challenge of accurately interpreting nuanced student emotions and the need for comprehensive data collection methods.
Definitions of Terms
• Sentiment Analysis: The process of using AI and natural language processing to analyze and categorize opinions, emotions, or sentiments from text.
• Artificial Intelligence (AI): Technology that enables machines to simulate human-like intelligence.
• Student Satisfaction: A measure of students' overall contentment with their university experience, including academic and non-academic aspects.
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