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ENGINEERING FACULTY / COMPUTER ENGINEERING / BLM4016 - SOCIAL MEDIA ANALYSIS
GENERAL INFORMATION ABOUT THE COURSE
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Course Objective
The goal is to equip students with the skills to extract meaningful insights from large-scale data collection, cleaning, and processing on social media platforms using modern analysis methods such as Social Network Analysis (SNA), Sentiment Analysis, and Topic Modeling, and to apply these insights in various fields such as marketing, politics, and crisis management.
Brief Content of the Course
The course begins with an introduction to social media analysis, data sources (APIs, crawling, scraping), and ethical issues. It then covers data preprocessing techniques such as noise cleaning, missing data management, and text processing (tokenization, stemming). Built on the foundations of graph theory, the course focuses on Social Network Analysis (nodes, edges, centrality measures - degree, betweenness, eigenvectors) and community detection. Furthermore, Sentiment Analysis (dictionary and machine learning-based) for identifying emotions in text data and Topic Modeling algorithms (LDA, NMF) for uncovering latent topics are explored. Practical work is supported by the use of visualization and analysis tools such as Gephi.
Prerequisites
None
Course Objectives
Course Objectives
1
Ability to explain Social Media Analysis components
2
Ability to perform Social Media Analysis on the desired topic
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Course Category
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Basic Vocational Courses
Expertise /Field Courses
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General Knowledge/General Aptitude Course