教學大綱表 (112學年度 第2學期)
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課程名稱
Course Title
(中文) 文字探勘
(英文) Text Mining
開課單位
Departments
資訊經營研究所
課程代碼
Course No.
N5540
授課教師
Instructor
胡志堅
學分數
Credit
3.0 必/選修
core required/optional
選修 開課年級
Level
研究所
先修科目或先備能力(Course Pre-requisites):For those who have a basic understanding of computer fundamentals, data structures, and programming.
課程概述與目標(Course Overview and Goals):With the rise of the Internet and social media, vast amounts of diverse textual data are being generated. Extracting, processing, and analyzing this unstructured data to create valuable information applications has become increasingly important. The Text Mining course combines theoretical foundations with practical exercises, enabling learners to understand and apply text mining techniques effectively using information tools.
教科書(Textbook) Kwartler, T. (2017). Text mining in practice with R. John Wiley & Sons
參考教材(Reference) Chen, Y. (2021). An automatic storytelling system based on natural language processing. (https://www.diva-portal.org/smash/record.jsf?pid=diva2:1571986)
生成式人工智慧導論與實作──ChatGPT與圖像生成實務應用, 新文京開發出版股份有限公司, 胡志堅編著 (2024), (ISBN:978-626-392-003-3)
https://www.wun-ching.com.tw/book_detail.asp?seq=13321
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

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
Pre-processing in Text Mining
Text Mining using Classification
Text Mining using Clustering
Introduce the impact of unstructured text on text analysis accuracy and the pre-processing phase in organizing documents for successful text analysis implementation. 講授
實作
 
7 An E-mail 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 seeing-sailing-selecting 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 TF-IDF Algorithm Text Mining Process Data Import (Building Corpus)
Text Processing and Data Cleaning
Tokenization (Chinese)
Building Document-Term Matrix (DTM)
Word Cloud
TF-IDF Algorithm
This unit allows students to learn the text mining process, including data import (building corpus), text processing and data cleaning, tokenization (Chinese), building document-term matrix, word cloud, and TF-IDF 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
Hands-on project
Presentation
Assessment in Case Studies includes hands-on 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
 
彈性教學週活動規劃

No.
實施期間
Period
實施方式
Content
教學說明
Teaching instructions
彈性教學評量方式
Evaluation
備註
Notes
1 起:2024-05-01 迄:2024-05-31 5.小專題 Project Students will work on projects in the field of text mining, involves the following steps: 1 Selecting topics 2 Implementing the selected topics 3 Completing reports 4 Delivering oral presentations Implementing and completing a written report in Word, as well as a PowerPoint presentation. Additionally, students are required to finalize their work by delivering an oral presentation.


教學要點概述:
1.自編教材 Handout by Instructor:
■ 1-1.簡報 Slids
■ 1-2.影音教材 Videos
□ 1-3.教具 Teaching Aids
□ 1-4.教科書 Textbook
□ 1-5.其他 Other
■ 2.自編評量工具/量表 Educational Assessment
□ 3.教科書作者提供 Textbook

成績考核 Performance Evaluation: 期末考:15%   期中考:15%   其他評量:10%   報告:30%   彈性教學:10%   上機測驗:10%   作業:10%  

教學資源(Teaching Resources):
□ 教材電子檔(Soft Copy of the Handout or the Textbook)
■ 課程網站(Website)
課程網站(Website):Tronclass
扣考規定:https://curri.ttu.edu.tw/p/412-1033-1254.php