CMP614 - TEXT MINING
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| TEXT MINING | CMP614 | Any Semester/Year | 3 | 0 | 3 | 9 |
| Prequisites | None | |||||
| Course language | English | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Preparing and/or Presenting Reports Problem Solving Project Design/Management | |||||
| Instructor (s) | Asst. Prof. Dr. Gönenç Ercan, Prof. Dr. İlyas Çiçekli | |||||
| Course objective | 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. | |||||
| Learning outcomes |
| |||||
| 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. | |||||
| References | 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 outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to Text Mining |
| Week 2 | Basic Techniques for processing Unstructured Text |
| Week 3 | Dimensionality Reduction, Latent Semantic Analysis |
| Week 4 | Topic Models: Latent Dirichlet Allocation |
| Week 5 | Topic Models: Statistical Models |
| Week 6 | Pattern based information extraction methods |
| Week 7 | Basic Techniques for processing Semi-structured texts. |
| Week 8 | Web Site Scraping and Wrapper Induction |
| Week 9 | Graph Based Methods |
| Week 10 | Graph Based Methods (continued) |
| Week 11 | Text Information Visualization |
| Week 12 | Topic segmentation and summarization |
| Week 13 | Sentiment and Opinion Analysis |
| Week 14 | Project presentations |
| Week 15 | |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Project | 1 | 50 |
| Seminar | 0 | 0 |
| Midterms | 0 | 0 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 1 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 14 | 10 | 140 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 60 | 60 |
| Homework assignment | 0 | 0 | 0 |
| Midterms (Study duration) | 0 | 0 | 0 |
| Final Exam (Study duration) | 1 | 30 | 30 |
| Total Workload | 30 | 103 | 272 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Graduates should have a mastery of computer science as described by the core of the Body of Knowledge. | X | ||||
| 2. Graduates need understanding of a number of recurring themes, such as abstraction, complexity, and evolutionary change, and a set of general principles, such as sharing a common resource, security, and concurrency. | X | ||||
| 3. Graduates of a computer science program need to understand how theory and practice influence each other. | X | ||||
| 4. Graduates need to think at multiple levels of detail and abstraction. | X | ||||
| 5. Students will be able to think critically, creatively and identify problems in their research. | X | ||||
| 6. Graduates should have been involved in at least one substantial project. | X | ||||
| 7. Graduates should realize that the computing field advances at a rapid pace. | X | ||||
| 8. Graduates should conduct research in an ethical and responsible manner. | X | ||||
| 9. Graduates should have good command of technical terms in both Turkish and English. | X | ||||
| 10. Graduates should understand the full range of opportunities available in computing. | X | ||||
| 11. Graduates should understand that computing interacts with many different domains. | X | ||||
| 12. Graduates should develop the knowledge acquired at master level and apply scientific methods in order to solve scientific problems. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest