WMK00700 Text Analysis

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program

Lecture Code WMK00700 Subject Classification Specialized Education

Subject Name テキスト解析

Subject Name
（Katakana）テキストカイセキ

Subject Name in
English Text Analysis

Instructor SUZUKI YOSHIHISA,KAJIKAWA HIROAKI,HARADA YUSUKE

Instructor
(Katakana) スズキ　ヨシヒサ,カジカワ　ヒロアキ,ハラダ　ユウスケ

Campus Higashi-Senda Semester/Term 1st-Year, Second Semester, Second Semester

Days, Periods, and Classrooms (2nd) Weds13-14

Lesson Style Lesson Style
(More Details) Face-to-face, Online (on-demand)

Credits 2.0 Class Hours/Week 2 Language of Instruction J : Japanese

Course Level 5 : Graduate Basic

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 05 : Sociology

Eligible Students Social Scienses Data Science Program

Keywords Text analytics, Machine Learning, Data science, AI, Social Sciences, Education

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)

Class Objectives
/Class Outline Learn the basics of text format analysis methods using basic programming and analysis software.
In this class, students systematically learn text analysis methods for extracting useful information from text data, focusing on programming exercises in Python and covering morphological analysis, word frequency analysis, sentence classification using machine learning, clustering, and topic models. GUI-based text analysis tools (e.g., KH Coder, Voyant Tools) will also be used to provide an easy approach for students who are not comfortable writing code. In the second half of the class, advanced methods such as neural networks (e.g., Word2Vec, BERT, etc.) and an overview of large-scale language models will be covered.

Class Schedule Session 1: Orientation and preparation of development environment
Session 2: Basics of Python and handling text data
Session 3: Basics of Text Analysis (1) - Morphological Analysis and Tokenization
Session 4: Basics of Text Analysis (2) - Corpus and Data Structure
Session 5: Statistical Basics (1) - Descriptive Statistics and Visualization
Session 6: Statistical Basics (2) - Statistical Hypothesis Testing
Session 7: Machine Learning (1) - Text Classification and Naive Bayes
Session 8: Machine Learning (2) - Information Retrieval and Web Text Collection
Session 9: Multivariate Analysis (1) - Clustering and Dimensionality Reduction
Session 10: Multivariate Analysis (2) - Topic Modeling (LDA) and Applications
Session 11: Neural Networks (1) - Word Embedding and Vector Representation
Session 12: Neural Networks (2) - Transformer Models such as BERT
Session 13: Application Exercises (1) - Comprehensive Analysis Using Text Analysis Software
Session 14: Application Exercise (2) - Project Work
Session 15: Summary and discussion of advanced cases and social issues

Students are required to submit reports.

Text/Reference
Books,etc. The textbooks will be assigned in the class.

PC or AV used in
Class,etc. Text, Visual Materials, Microsoft Teams, moodle

(More Details)

Learning techniques to be incorporated Discussions, Post-class Report

Suggestions on
Preparation and
Review Session 1: Please understand the flow of the class. Ensure that you have the right environment.
Session 2: Deepen your understanding of Python.
Session 3-4: Understand the structure of the text data you will be working with.
Session 5-6: Basic statistics.
Sessions 7-12: Deepen your understanding of data analysis methods and visualization.
Sessions 13-14: Deepen your understanding of pre-processing and textual data analysis based on real data.
Sessions 14-15: Review and discuss cutting-edge case studies.

Requirements

Grading Method Comprehensive evaluation of attitude to class and report

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	Graduate School of Humanities and Social Sciences (Master's Course) Division of Humanities and Social Sciences Social Data Science Program
Lecture Code	WMK00700	Subject Classification	Specialized Education
Subject Name	テキスト解析
Subject Name （Katakana）	テキストカイセキ
Subject Name in English	Text Analysis
Instructor	SUZUKI YOSHIHISA,KAJIKAWA HIROAKI,HARADA YUSUKE
Instructor (Katakana)	スズキ　ヨシヒサ,カジカワ　ヒロアキ,ハラダ　ユウスケ
Campus	Higashi-Senda	Semester/Term	1st-Year, Second Semester, Second Semester
Days, Periods, and Classrooms	(2nd) Weds13-14
Lesson Style		Lesson Style (More Details)	Face-to-face, Online (on-demand)

Credits	2.0	Class Hours/Week	2	Language of Instruction	J : Japanese
Course Level	5 : Graduate Basic
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	05 : Sociology
Eligible Students	Social Scienses Data Science Program
Keywords	Text analytics, Machine Learning, Data science, AI, Social Sciences, Education
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)
Class Objectives /Class Outline	Learn the basics of text format analysis methods using basic programming and analysis software. In this class, students systematically learn text analysis methods for extracting useful information from text data, focusing on programming exercises in Python and covering morphological analysis, word frequency analysis, sentence classification using machine learning, clustering, and topic models. GUI-based text analysis tools (e.g., KH Coder, Voyant Tools) will also be used to provide an easy approach for students who are not comfortable writing code. In the second half of the class, advanced methods such as neural networks (e.g., Word2Vec, BERT, etc.) and an overview of large-scale language models will be covered.
Class Schedule	Session 1: Orientation and preparation of development environment Session 2: Basics of Python and handling text data Session 3: Basics of Text Analysis (1) - Morphological Analysis and Tokenization Session 4: Basics of Text Analysis (2) - Corpus and Data Structure Session 5: Statistical Basics (1) - Descriptive Statistics and Visualization Session 6: Statistical Basics (2) - Statistical Hypothesis Testing Session 7: Machine Learning (1) - Text Classification and Naive Bayes Session 8: Machine Learning (2) - Information Retrieval and Web Text Collection Session 9: Multivariate Analysis (1) - Clustering and Dimensionality Reduction Session 10: Multivariate Analysis (2) - Topic Modeling (LDA) and Applications Session 11: Neural Networks (1) - Word Embedding and Vector Representation Session 12: Neural Networks (2) - Transformer Models such as BERT Session 13: Application Exercises (1) - Comprehensive Analysis Using Text Analysis Software Session 14: Application Exercise (2) - Project Work Session 15: Summary and discussion of advanced cases and social issues Students are required to submit reports.
Text/Reference Books,etc.	The textbooks will be assigned in the class.
PC or AV used in Class,etc.	Text, Visual Materials, Microsoft Teams, moodle
(More Details)
Learning techniques to be incorporated	Discussions, Post-class Report
Suggestions on Preparation and Review	Session 1: Please understand the flow of the class. Ensure that you have the right environment. Session 2: Deepen your understanding of Python. Session 3-4: Understand the structure of the text data you will be working with. Session 5-6: Basic statistics. Sessions 7-12: Deepen your understanding of data analysis methods and visualization. Sessions 13-14: Deepen your understanding of pre-processing and textual data analysis based on real data. Sessions 14-15: Review and discuss cutting-edge case studies.
Requirements
Grading Method	Comprehensive evaluation of attitude to class and report
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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