N5540 教學大綱表

教學大綱表 (113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
序 No.	單元主題 Unit topic	內容綱要 Content summary	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
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

序 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:2025-05-01 迄:2025-06-15	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.	無

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