Implementation of Sentiment Analysis in University Student Feedback Systems: A Case Study of Federal University, Dutse (Dutse LGA, Jigawa State)
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- NGN 5000
Background of the Study
University student feedback systems are an essential tool for collecting opinions and suggestions from students regarding various aspects of university life, including teaching quality, course content, and campus facilities. Traditionally, feedback is collected through surveys, which are then analyzed manually. However, this approach is time-consuming and may not provide real-time insights into student sentiments.
With the advancement of natural language processing (NLP) and sentiment analysis techniques, there is an opportunity to implement AI-driven sentiment analysis in student feedback systems. Sentiment analysis can automatically categorize feedback as positive, negative, or neutral, providing university administrators with a clearer understanding of student sentiments and enabling more effective decision-making. This study will focus on implementing sentiment analysis in the student feedback system at Federal University, Dutse, located in Dutse LGA, Jigawa State.
Statement of the Problem
The current student feedback system at Federal University, Dutse lacks efficiency in analyzing large volumes of feedback data. Manual analysis of feedback results in delayed responses to student concerns and challenges in identifying common trends and sentiments. Implementing an AI-based sentiment analysis system could improve the speed and accuracy of feedback analysis, leading to better responses and more informed decision-making.
Objectives of the Study
1. To implement a sentiment analysis system for the student feedback system at Federal University, Dutse.
2. To evaluate the effectiveness of the sentiment analysis system in categorizing student feedback accurately.
3. To assess the impact of AI-driven sentiment analysis on improving decision-making processes in university management.
Research Questions
1. How can sentiment analysis be implemented in the student feedback system at Federal University, Dutse?
2. How accurate is the AI-driven sentiment analysis system in categorizing student feedback?
3. What impact does the implementation of sentiment analysis have on the university's response time to student concerns?
Research Hypotheses
1. The implementation of sentiment analysis will significantly improve the accuracy and speed of categorizing student feedback at Federal University, Dutse.
2. AI-driven sentiment analysis will help university administrators make better-informed decisions based on student feedback.
3. The introduction of sentiment analysis in feedback systems will lead to higher student satisfaction with the university’s responsiveness.
Significance of the Study
This study will demonstrate how AI-powered sentiment analysis can enhance the student feedback process, enabling quicker and more accurate responses to student concerns. The findings will help Federal University, Dutse improve its student services and management practices, ultimately enhancing the overall student experience.
Scope and Limitations of the Study
The study will focus on the implementation of sentiment analysis in the student feedback system at Federal University, Dutse, located in Dutse LGA, Jigawa State. The research will be limited to feedback data collected through surveys and will not explore other forms of feedback such as direct interviews or informal complaints.
Definitions of Terms
• Sentiment Analysis: A technique in natural language processing that involves analyzing text to determine whether the sentiment expressed is positive, negative, or neutral.
• Natural Language Processing (NLP): A field of AI that enables computers to understand, interpret, and manipulate human language.
• Student Feedback System: A system used by universities to collect and analyze students' opinions and evaluations of various aspects of university life.
• AI-driven Decision Making: The use of artificial intelligence to support or automate decisions based on data analysis and pattern recognition.
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