[NetMiner 5] Text Analysis: Finding Key Topics

"A first step for beginners using NetMiner 5—from basic usage to analyzing research data, all in one!&quot

"How to Quickly Identify Social Opinion Trends! Training in News and Community Data Analysis" • News data contains important information such as the latest events, economic changes, political trends, and natural disasters, enabling companies or individuals to quickly identify and understand these changes. Online community data consists of text data containing users’ opinions and information on various topics, providing social and cultural insights. • News and community data, along with text analysis and text network analysis methods, can be used for various purposes, including market analysis, policy and decision-making, public response analysis, trend analysis, education and research, crisis response, and disaster management.

This course teaches how to collect, model, and analyze social media data using NetMiner. It covers how to collect and organize YouTube data using NetMiner’s SNS Data Collector, understand and model the types and structures of social media data, and analyze social media influence, topics, and trends through theory, case studies, and hands-on practice.

This course teaches how to identify research trends through social network analysis by directly collecting and preprocessing paper data. Through hands-on practice, you will learn how to collect data using the Bibliographic Information Collector (Biblio Data Collector), transform a two-mode relationship connecting “papers” and “authors” into a one-mode network connecting “authors” and “authors,” and extract words from unstructured text data such as abstracts, followed by keyword network analysis and visualization.

This course uses Python, a programming language well suited for big data analysis, to directly collect, process, and analyze social big data from the web, social media, and other sources. Python programming exercises are conducted in a step-by-step, follow-along format, making it easy for humanities and social science researchers with no programming experience to learn. By completing this course, you will be able to master Python, collect data, and analyze big data.

This course covers methods for analyzing unstructured text data, such as news articles, tweets, and speeches, using NetMiner’s text network analysis features. You will build a theoretical foundation in the entire text network analysis process, from extracting words from unstructured text data and discovering topics to constructing and visualizing word networks and selecting key keywords. You will also practice applying these analytical methods to real-world news article and Twitter data using NetMiner.

Although the majority of real-world data consists of '2-mode' relationships—such as buyer–product, user–content, and word–document relationships—have you encountered distorted metrics or limitations in data transformation from blindly applying conventional 1-mode analysis methods? We present clear solutions ranging from the core principles of 2-mode networks to data conversion and direct analysis (Direct/Bipartite) approaches, as well as hands-on practice in user profiling and association analysis using NetMiner. Spend less time struggling with data conversion and metric calculation, and take your research and professional insights to the next level with unparalleled 2-mode network analysis capabilities spanning text, bibliographic, and SNS data.

• This course introduces methodologies for analyzing roles and positions within networks—topics that have been extensively studied in SNA—and helps participants understand the underlying principles through hands-on exercises using NetMiner, while also exploring practical applications. • Through this course, participants can measure brokerage roles from various perspectives, determine actors’ roles and positions within a network based on relationship patterns, and summarize the entire network through the relationships among role groups, thereby uncovering the key structures that comprise complex networks.

This course covers how to statistically interpret the results of network data analysis and how to test the significance of statistical measures. When applying traditional statistical methods such as cross-tabulation, analysis of variance, correlation analysis, and regression analysis, researchers need to understand how the characteristics of network data and network indices affect significance testing and use appropriate methods. The course consists of examples covering analytical topics frequently encountered by researchers—such as analyzing associations between variables, testing the significance of network indices, and analyzing associations among multiple networks—along with hands-on exercises using NetMiner.

A group in which entities are densely connected within a network is called a cohesive group. This course explores in detail the frequently used analysis methods for identifying cohesive groups with many connections, including cliques, k-cores, components, and communities. Through this course, discover key groups in network data or identify closely connected clusters.

Network centrality refers to the importance of a particular node within a network. There are various ways to measure it, and this course provides an in-depth look at seven representative and widely used centrality indices, along with analyses using real-world data. Through the course, learn how to select and apply centrality indices suited to various types of network data.

The most basic approach in network data analysis is to analyze network characteristics. Starting with the frequencies of nodes and links, this course explores in detail the principles and meanings of various indices that describe structural features of networks, including the distribution of the number of connections, density, shortest distance, clustering coefficient, and more. If the structure of a network can be measured numerically, it becomes possible to compare it with other networks. Through this course, define the characteristics of your network data from various perspectives.

To visually inspect network data, explore its structure, and effectively present analytical results, you need to understand and apply a variety of visualization methods. NetMiner provides features that effectively represent network data and analysis results. Through this course, learn how to present your network analysis results aesthetically and communicate the key points effectively.

To obtain accurate and meaningful results and insights through network analysis, data must be processed appropriately according to the direction and methods of the analysis. This course explains the essential preprocessing methods you need to know when preparing and entering network data, and provides hands-on practice with various data cleaning, extraction, and transformation methods using NetMiner and real-world data.

Starting with the question “What are social networks and social network analysis (SNA)?,” this course explains the key theories and analytical methods related to them, the specific process of understanding network data and constructing it from observed phenomena, and the research design process. As the first step in social network analysis, data modeling is highly important. Through this course, gain a thorough understanding of “network data,” which may be unfamiliar, and try designing a process for creating or collecting network data directly from a topic of interest.

How can you analyze data in research papers and identify research trends? This course covers quantitative research trend analysis, from collecting bibliographic information to network analysis and topic modeling, using NetMiner 5 and Biblio Data Collector. By developing bibliographic data processing skills and network analysis techniques, you can identify research trends objectively and efficiently.

"Social Network Analysis Training for Immediate Use in Practice and Research" Would you like to analyze the “relationships” within organizations, society, and content as data? Social network analysis (SNA) is a powerful analytical technique for structurally understanding relationships among people, information, and systems and uncovering hidden insights. From the core theories of social network analysis to hands-on practice, use NetMiner 5 and apply what you learn directly to your work and research.

This course introduces the analysis tool NetMiner and covers its basic usage. Through hands-on practice with opening and editing data files, importing external data, and performing visualization and analysis, it helps reduce the apprehension and difficulty experienced by those encountering NetMiner for the first time.

From the basics to applications of unstructured data analysis Easily and quickly with the AI analysis tool Orange!

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

Do you want to become a data analytics expert of the future? Grab your chance now! This is a unique opportunity to explore infinite possibilities and learn how to derive powerful insights through cutting-edge artificial intelligence technology and big data.

"A first step for beginners using NetMiner 5—from basic usage to analyzing research data, all in one!&quot

Master the Big Data Analysis Certification—an essential credential that is by no means easy to self-study—in a short period using R, an easy-to-learn data analysis tool!

🧩 Rather than focusing on complex formulas or theory-centric explanations, the goal is to learn the core concepts of recommendation systems by directly implementing programs. 🛠️ Through a total of 12 diverse and practical examples, we have progressively designed recommendation systems that can be utilized in real-world environments, including content-based recommendation, collaborative filtering, and deep learning recommendation.

ADsP Summary + Past Exam Solutions. Reflects August 2025 46th Exam! (Continuously Updated!)

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.

ADsP Certification All-In-One Course! Ultra-Short Completion! Reference Book: "Everyone's ADsP Cheat Sheet" https://wikidocs.net/book/18829

"Beyond Data Analysis: Mastering Predictive Modeling with 5 Real-World Projects (45 Lectures Total)" Have you finished learning data analysis but feel stuck when it's time to actually build a model? Beyond simply learning how to call libraries, this course will help you fully master the inner workings of algorithms and optimal model validation strategies—ranging from Titanic survival prediction to spam text classification. Systematically conquer projects that wield the most power in the industry, from linear models to the latest ensemble algorithms and the basics of Natural Language Processing (NLP). Step into the world of AI modeling and start predicting the future based on analyzed data.

Python Korean Text Analysis and Natural Language Processing: Word Cloud Visualization, Morphological Analysis, Topic Modeling, Clustering, Similarity Analysis, Bag of Words and TF-IDF for Text Data Vectorization, Text Classification Using Machine Learning and Deep Learning, and How to Use Hugging Face

"Data analysis—don't stay trapped in complex Python syntax and Excel functions anymore." This course is a practical masterclass that automates the entire data processing journey—from Excel and Google Sheets to Python-integrated analysis—by hiring the latest AI agents as your personal dedicated data analysts. Instead of difficult coding knowledge, we introduce the latest 'Vibe Coding' method, where you give commands as if having a natural language conversation. This allows non-majors and humanities students to enter the world of data analysis without any barriers. Experience a powerful AI workflow that can be applied to your actual work tomorrow through comprehensive hands-on practice using real business data, covering everything from data preprocessing and visualization to report writing. A lecture on utilizing AI agents, Excel data analysis, chatGPT, Gemini, (Python), Genspark, Google Colab, chatbots, Google Antigravity, and notebookLM.

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

If you're wondering, "I did the user research... but is this result truly representative of all users?" then pay attention! We will show you exactly how to add persuasiveness to your designs based on data. ✅ We explain complex statistical terms in very simple language. ✅ We carefully introduce the 3 core hypothesis tests essential for UX practice, covering everything from principles to hands-on practice. ✅ We show you how to easily analyze user data using AI—without costs or hallucinations.

Learn how to process big data from a Silicon Valley software engineer & how to develop big data code with Apache Spark using Python. I am a 14-year software developer who handles everything from web applications to big data and SRE & DevOps with Python. Don't miss this opportunity to learn about Apache Spark, which is essential for big data professionals, in an easy and in-depth way using Python!

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

"Data analysis—don't stay trapped in complex Python syntax and Excel functions anymore." This course is a practical masterclass that automates the entire process of data processing, from Excel and Google Sheets to Python-linked analysis, by hiring the latest AI agents as your own dedicated data analysts. Instead of difficult coding knowledge, we introduce the latest 'Vibe Coding' method—commanding as if having a conversation in natural language—allowing even non-majors to enter the world of data analysis without any barriers. Through comprehensive hands-on practice using actual business data—covering everything from data preprocessing and visualization to report writing—experience a powerful AI workflow that you can apply to your work starting tomorrow. A lecture utilizing AI agents, Excel data analysis, chatGPT, Gemini, (Python), Genspark, Google Colab, chatbots, Google Antigravity, and notebookLM.

Are you struggling with inefficiencies caused by fragmented AI tools and manual tasks? Drawing on the expertise I’ve gained through hands-on experience, I’ll build integrated automation workflows to maximize your productivity.

"Why does my automated trading collapse in a bear market?" I have also deeply pondered this same question. Most automated trading logics on the market seem to generate profits during a bull market, but they lose their ability to respond the moment the market sentiment changes. To solve this problem, I have combined the know-how I researched myself over the past two years with the latest AI technology. In this course, you will learn how to directly design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and individual stock flows, and how to automatically train and deploy it in a free GPU environment. In particular, we will introduce sophisticated data normalization techniques at the hedge fund level to build an 'Intelligent Comprehensive Scoring System' that goes beyond simple indicators to incorporate news sentiment. To this, we add an 'AI Safety Gate' that implants the insight of a trader with 20 years of experience on Wall Street, adding a final layer of confidence from a human trader's perspective right before a purchase. Beyond simple buying and selling functions, you will directly implement the entire process of an institutional-grade algorithmic system by adding dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.