課程大綱 Syllabus 
學生學習目標 Learning Objectives 
單元學習活動 Learning Activities 
學習成效評量 Evaluation 
備註 Notes 
序 No.  單元主題 Unit topic 
內容綱要 Content summary 
1  Overview of Text Mining  Unlocking the Value of Unstructured Data 
Definition of Text Mining
Importance and Applications
Brief Historical Context 
This chapter allows students to understand the course structure and learn the basic concepts of text mining and big data. 
講授



2  Tools and Software for Text Mining 
Overview of Popular Tools: R, Python, RapidMiner
Demonstration of Basic Text Mining in R or Python 
This chapter enables students to learn about the concepts, methods, and tools of data science, as well as understand the similarities and differences between text mining and data mining. 
講授



3  Text Mining in Big Data Analytics (I) 
Introduction
Text Mining in Transcripts and Speeches
Blog Mining
Email Mining
Web Mining 
This chapter allows students to learn about the operational process and techniques of text mining, and to understand the development trends of text mining. 
講授 實作



4  Text Mining in Big Data Analytics (II) 
Social Media
Published Articles
Meeting Transcripts
Knowledge Extraction
Conclusions 
This chapter aims to help students understand how text mining is applied in Social Media, Published Articles, Meeting Transcripts, and Knowledge Extraction. 
講授 實作



5  Text Mining Methods and Techniques 
Introduction
Challenging Issues
Methods and Models used in Text Mining
Techniques used in Text Mining 
Introduce the Text Mining Steps:
1. Collecting information from unstructured data.
2. Convert this information received into structured data
3. Identify the pattern from structured data
4. Analyze the pattern
5. Extract the valuable information and store in the database. 
講授 實作



6  Text Mining in Social Networks 
Introduction
Preprocessing in Text Mining
Text Mining using Classification
Text Mining using Clustering 
Introduce the impact of unstructured text on text analysis accuracy and the preprocessing phase in organizing documents for successful text analysis implementation. 
講授 實作



7  An Email Analysis Method based on Text Mining Techniques  Literature Reviewing and Presenting 
Introduction
Abstract
Practice: Literature Reviewing
Practice: Presentations
Q & A 
This unit aims to guide students to read research articles and perform their arguments. 
講授 實作



8  Midterm Exam & Midterm Report 
Midterm Exam
Midterm Report & Presentation 
This unit can assess students' learning outcomes. 
上機實習 實作 心得發表

報告 期中考


9  R Basic 
1. Features of R
2. R – Basic Syntax
3. R Script File
4. Comments
5. R – Data Types 
This unit aims to teach students the basic functionalities of the R language and programming skills. 
講授 實作

作業 上機測驗


10  Developing Navigation Graphs for TED Talks  Text Mining Applications and Information Visualization 
Related work
Development of Navigation Graphs
Seeing – Sailing  Selecting on Navigation Graphs
Experimental Evaluations 
This chapter introduces TED Talks navigation graphs, enabling users to explore videos based on query keywords. Learners will understand how to integrate text mining and information visualization to navigate from a selected video through related content using the seeingsailingselecting approach. 
講授 實作



11  Text Mining and R Language 
Handling Text and String Data
Basics of Text and String Data in R
Introduction to Text Mining in R 
This unit enables students to learn about handling text and string data, basics of text and string data in R, and using text mining tools in R. 
講授 實作



12  Text Mining Process and TFIDF Algorithm 
Text Mining Process Data Import (Building Corpus)
Text Processing and Data Cleaning
Tokenization (Chinese)
Building DocumentTerm Matrix (DTM)
Word Cloud
TFIDF Algorithm 
This unit allows students to learn the text mining process, including data import (building corpus), text processing and data cleaning, tokenization (Chinese), building documentterm matrix, word cloud, and TFIDF algorithm. 
講授 實作

作業 上機測驗


13  Calculation and Application of Distance Validity and Cosine Similarity 
Hamming Distance
Euclidean Distance
Manhattan Distance
Jaccard Index
Cosine Similarity 
This unit allows students to learn the calculation and application of various distance validity metrics, including Hamming Distance, Euclidean Distance, Manhattan Distance, Jaccard Index, and Cosine Similarity. 
講授 實作



14  Case Study  Text Mining & Data Visualization 
Interactive Data Visualization for Text Mining Program Implementation 
This unit enables students to learn the technical skills of text mining data visualization through case studies, fostering the achievement of learning objectives. 
上機實習 講授 實作

上機測驗


15  Case Study  Application of Text Mining Techniques in Sentiment Analysis 
Research on Using Text Mining Techniques in Sentiment Analysis 
This unit enables students to learn how to apply text mining techniques to sentiment analysis through case studies, promoting the achievement of learning objectives. 
上機實習 講授 實作

作業 上機測驗


16  Final Exam & Final Exam Report 
Practices in Case Studies
Handson project
Presentation 
Assessment in Case Studies includes handson projects and presentations, allowing students to apply their learning outcomes in practical scenarios and demonstrate their understanding effectively. 
講授 實作 探索體驗

上機測驗 期末考 彈性教學

