Sentiment Analysis and Opinion Mining
Information Systems Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Sentiment Analysis and Opinion Mining | BTM549 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Associate Prof. Dr. Zeynep Hilal KİLİMCİ
Research Assistant M.M. Enes YURTSEVER
Learning Outcomes of the Course Unit
1) Understand the concept of sentiment analysis and its close relationship with statistical natural language processing
2) Learns basic sentiment analysis and opinion mining
3) Learns sentiment classifications and appearance models
4) Learns the detection of spam
5) Learns basic classification algorithms and their applications in sentiment classification
6) Learns 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 | ||
| Learning Outcomes | ||||||||
| 1 | No relation | Middle | No relation | No relation | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 4 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 5 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
| 6 | No relation | No relation | No relation | No relation | No relation | No relation | 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
Since the field of emotion/sentiment analysis covers a very large problem area, the subject areas are intended to be different from linguistic models. It is aimed to develop lexical resources and automatic idea extraction, mining and classification systems.
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- Foundations of Statistical Natural Language Processing, by C. Manning and H. Schütze (2003).
2- Introduction to Information Retrieval, Manning, Raghavan and Schütze, Cambridge University Press (2008)
3- Mining the Web: Discovering Knowledge from Hypertext Data, Chakrabarti (2003)
4- Information Retrieval: A book by C. J. van RIJSBERGEN
5- Bing Liu, Sentiment Analysis (Mining Opinions, Sentiments, and Emotions) 1st Edition, ISBN-13: 978-1107017894 ISBN-10: 1107017890
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Not Required