KA240101 Text Mining

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School School of Informatics and Data Science

Lecture Code KA240101 Subject Classification Specialized Education

Subject Name テキストマイニング

Subject Name
（Katakana）テキストマイニング

Subject Name in
English Text Mining

Instructor DAI YILING

Instructor
(Katakana) ダイ　オクリョウ

Campus Higashi-Hiroshima Semester/Term 3rd-Year, Second Semester, 4Term

Days, Periods, and Classrooms (4T) Mon1-4：ECON B257

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic.

Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Abilities and Skills）
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline Text mining refers to the field of extracting knowledge from large volumes of textual data, which has been advanced alongside the development of natural language processing techniques. In this lecture, you are supposed to learn the fundamental theories and applications of text mining using Python. In the first part, you will learn the basics through familiar examples. In the second part, you will learn the main techniques such as classification, topic modeling, information retrieval, summarization, and sentiment analysis. In the third part, we will explore the impact of large language models on text mining. Finally, in the fourth part, you are going to apply the techniques then present the results as the course final assignment.

Class Schedule lesson1 Introduction to text mining: Human vs Machine (Discussion task)
lesson2 History of text mining, preparation of python environment
lesson3 Preprocessing and representation of texts
lesson4 Basic statistics and visualization of texts
lesson5 Cooccurrences of texts
lesson6 Classification of texts
lesson7 Topic modeling
lesson8 Information retrieval
lesson9 Summarization
lesson10 Review of the discussion task, release of the course assignment
lesson11 Sentiment analysis
lesson12 Nueral network, machine translation
lesson13 Language models
lesson14 Presentation of the course assignment
lesson15 Presentation of the course assignment

Basically, exercises are conducted every week.
The schedule may change according to the progress.

Text/Reference
Books,etc. No textbooks will be required for this course.
Handouts will be provided.

You are recommended to refer to the following books:
・金　明哲　著，テキストアナリティクスの基礎と実践，岩波書店，2021
・Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
・Charu C. Aggarwal, Machine Learning for Text, Springer, 2019.

PC or AV used in
Class,etc.

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review It is better to have some experience in Python or other programming languages.

Requirements

Grading Method The course grade will be determined by discussion, exercises, and the course assignment.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA240101	Subject Classification	Specialized Education
Subject Name	テキストマイニング
Subject Name （Katakana）	テキストマイニング
Subject Name in English	Text Mining
Instructor	DAI YILING
Instructor (Katakana)	ダイ　オクリョウ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Mon1-4：ECON B257
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework underlying computer science and to collect and process high-dimensional data through full use of information processing technology based on scientific logic. Data Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Abilities and Skills）・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. ・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	Text mining refers to the field of extracting knowledge from large volumes of textual data, which has been advanced alongside the development of natural language processing techniques. In this lecture, you are supposed to learn the fundamental theories and applications of text mining using Python. In the first part, you will learn the basics through familiar examples. In the second part, you will learn the main techniques such as classification, topic modeling, information retrieval, summarization, and sentiment analysis. In the third part, we will explore the impact of large language models on text mining. Finally, in the fourth part, you are going to apply the techniques then present the results as the course final assignment.
Class Schedule	lesson1 Introduction to text mining: Human vs Machine (Discussion task) lesson2 History of text mining, preparation of python environment lesson3 Preprocessing and representation of texts lesson4 Basic statistics and visualization of texts lesson5 Cooccurrences of texts lesson6 Classification of texts lesson7 Topic modeling lesson8 Information retrieval lesson9 Summarization lesson10 Review of the discussion task, release of the course assignment lesson11 Sentiment analysis lesson12 Nueral network, machine translation lesson13 Language models lesson14 Presentation of the course assignment lesson15 Presentation of the course assignment Basically, exercises are conducted every week. The schedule may change according to the progress.
Text/Reference Books,etc.	No textbooks will be required for this course. Handouts will be provided. You are recommended to refer to the following books: ・金　明哲　著，テキストアナリティクスの基礎と実践，岩波書店，2021 ・Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. ・Charu C. Aggarwal, Machine Learning for Text, Springer, 2019.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	It is better to have some experience in Python or other programming languages.
Requirements
Grading Method	The course grade will be determined by discussion, exercises, and the course assignment.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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