Sentiment Analysis
Information Systems Engineering
Faculty of Technology
First Cycle (Bachelor's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Sentiment Analysis | TBL438 | Elective | Bachelor's degree | 4 | Fall | 5 |
Name of Lecturer(s)
Associate Prof. Dr. Zeynep Hilal KİLİMCİ
Associate Prof. Dr. Serdar SOLAK
Learning Outcomes of the Course Unit
1) Understand the concept of emotional analysis and its close relationship with statistical natural language processing (SNLP).
2) To learn basic sentiment analysis and opinion mining
3) To learn sentiment classifications and appearance models
4) To learn spam detection
5) To learn basic classification algorithms and their applications in sentiment classification
6) To learn basic information extraction algorithms and their applications in sentiment analysis and opinion mining
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | ||
| Learning Outcomes | ||||||||||||
| 1 | No relation | Middle | Middle | No relation | No relation | Middle | No relation | No relation | Low | No relation | No relation | |
| 2 | Middle | Middle | Middle | No relation | No relation | Middle | No relation | No relation | Low | No relation | No relation | |
| 3 | High | High | High | No relation | No relation | High | Low | No relation | Low | Middle | No relation | |
| 4 | High | High | High | Low | Low | High | Middle | No relation | Low | Middle | No relation | |
| 5 | High | High | High | No relation | No relation | High | Low | No relation | Low | Middle | No relation | |
| 6 | Middle | Middle | Middle | No relation | No relation | Middle | No relation | No relation | Low | Low | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Statistics and Probability, Introduction to Machine Learning, Introduction to Text Mining
Course Contents
Subtraction of user's emotion analysis on different topics, - Modeling the classification methods required for the analysis of emotions, - Obtaining the data sets necessary for emotion analysis. - Using and programming the necessary tools for obtaining data sets
Weekly Schedule
1) Introduction to Emotion Analysis2) Introduction to Statistical Natural Language Processing (NLP)
3) Doküman Duygu Sınıflandırması
4) Doküman Duygu Sınıflandırması
5) Sentence Subjectivity and Sentiment Classification
6) Sentence Subjectivity and Sentiment Classification
7) Vision Based Emotion Analysis
8) Midterm Examination
9) Sentiment Dictionary Production
10) Idea Summarizing
11) Idea Summarizing
12) Comparative Idea Analysis
13) Project Presentations
14) Search and Receive Feedback
15) Geribildirim Spam Algılama
16) Final exam
Recommended or Required Reading
1- Bing Liu, Sentiment Analysis (Mining Opinions, Sentiments, and Emotions) 1st Edition, ISBN-13: 978-1107017894 ISBN-10: 1107017890
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Modelling
6) Group Study
7) Self Study
8) Project Based Learning
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required