Applications of Text Mining to Studying the Association between Responses and Opinions from Opinion Web System of the National Taiwan University
Date Issued
2016
Date
2016
Author(s)
Liao, Tzu-Han
Abstract
Advanced network information and growing mobile communications have resulted in an increase of publicly available data on the internet. This indicates the arrival of Big Data. According to statistics from the International Data Corporation （IDC）, the overall volume of data worldwide will reach 40 Zettabytes（ZB）or, 43 trillion Gigabytes（GB）by 2020. This will generate a 50-fold growth from 2010. The frequent use of unstructured information such as text, images, video and audio will also become greater. Specifically, the rise of the power of keyboard has made text-based communication an essential channel for the public to discuss and exchange information online. Aside from the commonly used quantitative analysis, qualitative data incorporates extensive information to provide additional value of analyzes. This study follows Yao’s work: “Statistical Analysis of the Data from University Assembly Meetings and the Opinion Web System of the National Taiwan University” and utilizes its quantitative analysis results to further conduct qualitative analysis on the National Taiwan University’s opinion web system. This research aims to search for hidden information in complicated unprocessed text through text mining which involves text segmentation, latent semantic analysis and sentiment analysis. By using these approaches, it examines the issues that students used the system to express their opinions; and whether the responses from the university effectively and adequately responded and resolved these issues. The research also examines whether both sides have actually communicated in a rational manner. To better the communication between students and the university on the opinion web system; this research used the technic of text mining to uncover the problems that occurred in the process of information exchange. It gives further recommendations for students to raise questions through rational and critical thinking and for the university to respond with a positive and genuine attitude. This can enhance the operations of the university and lead to a better development in the future.
Subjects
Data mining
Text mining
Word frequency
Word cloud
Text segmentation
Latent semantic analysis
Sentiment analysis
Type
thesis
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