| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Text Mining | YZM424 | Elective | Bachelor's degree | 4 | Spring | 5 |
Name of Lecturer(s)
Associate Prof. Dr. Hakan GÜNDÜZ
Associate Prof. Dr. Mehmet Zeki KONYAR
Assistant Prof. Dr. İrfan KÖSESOY
Learning Outcomes of the Course Unit
1) Defines and recalls the basic concepts of text mining and their close relationship with Statistical Natural Language Processing (SNLP).
2) Explains text mining techniques and understand their applications by comparing different techniques.
3) Installs and uses text mining tools.
4) Applies data preprocessing techniques on a given text dataset to form clean datasets.
5) Analyze text mining algorithms and evaluates their effectiveness.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
| Learning Outcomes | ||||||||||
| 1 | Low | Middle | High | No relation | High | High | No relation | Low | No relation | |
| 2 | Low | No relation | High | No relation | High | Low | No relation | Low | Low | |
| 3 | No relation | High | High | No relation | High | High | No relation | Low | Middle | |
| 4 | No relation | High | High | No relation | High | High | No relation | Low | Middle | |
| 5 | No relation | High | Middle | Middle | High | High | Middle | High | Middle | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Introduction to Text Mining: Jumble Text Data Mining Introduction to Statistical Natural Language Processing (NLP) Mathematical Foundations Linguistic Fundamentals and Corpus-Based Study Collocation Selection with Collocated Frequency, Hypothesis Tests, Mutual Information Statistical Inference: n-gram Models According to Sparse Data For data mining algorithms preparation. Cluster Classification Web page classification
Weekly Schedule
1) Introduction and Basic Concepts2) Text Preprocessing - 1
3) Text Preprocessing-2
4) Text Representation and Vectorization Techniques
5) Text Classification-1
6) Text Classification-2
7) Sentiment Analysis-1
8) Sentiment Anlaysis-2
9) Information Extraction and Summarization-1
10) Information Extraction and Summarization-2
11) Topic Modeling-1
12) Topic Modeling-2
13) Text Similarity and Information Retrieval
14) Project Presentations and Evaluation
15) Ethics and Fairness in Text Mining
Recommended or Required Reading
- Goyal, P., Pandey, S., & Jain, K. (2018). Deep learning for natural language processing: Creating neural networks with Python. Apress.
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Contribution of Project to Course Grade |
40% |
|---|---|
Contribution of Final Examination to Course Grade |
60% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required