Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN447Fundamentals of Text Mining3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Text data is the most common and vast of information digitally available today. These information sources are usually either non or semi structured. Methods for extracting information that can be processed by computer algorithms will be studied throughout this course.
Course Content Unstructured text processing methods
Topic models and statistical models.
Pattern based information extraction methods
Graph theory based text mining
Semantic Analysis.
Apllication of Natural Language Processing.
Course Methods and Techniques Lecture, discussion, question and answer, preparing and/or presenting reports, problem solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Prof. Suat Özdemir
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources 1. Charu Aggarwal and Cheng Xiang Zhei, "Mining Text Data", Springer, 2012. 2. Sholom Weiss, Nitin Indurkhya and Tong Zhang, "Fundamentals of Predictive Text Mining", Springer, 2010. 3. Ronen Feldman and James Sanger, "The Text Mining Handbook", Cambridge Press, 2007.
Course Notes Charu Aggarwal and Cheng Xiang Zhei, "Mining Text Data", Springer, 2012.

Sholom Weiss, Nitin Indurkhya and Tong Zhang, "Fundamentals of Predictive Text Mining", Springer, 2010.

Ronen Feldman and James Sanger, "The Text Mining Handbook", Cambridge Press, 2007.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Attendance 14 % 5
Project 1 % 30
Final examination 1 % 50
Sunum 1 % 15
Total
17
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Presentation 1 20 20
Project 1 35 35
General Exam Preparation 1 35 35
Total Work Load   Number of ECTS Credits 5,8 174

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing the course, the students will
2 Learn basic methods used in processing text data.
3 Learn statistical topic models and their uses in text mining.
4 Learn pattern based information extraction methods.
5 Learn to use Graph based methods in text mining.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Text Mining
2 Basic Techniques for processing Unstructured Text
3 Dimensionality Reduction, Latent Semantic Analysis
4 Topic Models: Latent Dirichlet Allocation
5 Topic Models: Statistical Models
6 Pattern based information extraction methods
7 Basic Techniques for processing Semi-structured texts.
8 Web Site Scraping and Wrapper Induction
9 Graph Based Methods
10 Graph Based Methods (continued)
11 Text Information Visualization
12 Topic segmentation and summarization
13 Sentiment and Opinion Analysis
14 Project presentations
15 Project presentations
16 Final Exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 4 3 4 3 3 4 2 2 1 1 4
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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