[Practical] TEXTOM Practical Lecture: Text Analysis/Text Mining for Big Data Thesis Writing
Instead of explanations focused on fundamental theories about 텍스톰(TEXTOM), we enable you to develop text mining and big data analysis capabilities through practical hands-on exercises.
I was looking for a lecture to study for my thesis and came across this lecture. I want to apply it step by step while practicing because it covers practical content. Thank you for the great lecture :D
5.0
해리
75% enrolled
This is a really effective lecture that not only provides theoretical information but also practical tips. I had fun studying as if I was receiving private tutoring!
5.0
김안나
99% enrolled
This was very helpful!!
What you will gain after the course
Big Data Analysis using TEXTOM
Practical examples of Big Data analysis
1:1 Coaching Example: Having trouble using Textom? 📊 This course will save you time!
Are you able to analyze text mining yourself after watching the text mining lectures available on the market?
“I would like to see examples or practical examples of applying the theory to real-world analysis rather than just theory.”
“We need a text mining course that will enable us to write papers on trend analysis, cognitive analysis, and other topics .”
👉 After seeing these reviews, I decided to film the lecture.
Big data analysis, which is a recent trend,
There is no field in business/research where it is not used.
Text mining is becoming an increasingly essential analytical method for research. As it becomes mainstream, many people are eager to learn it.
TextStorm for text mining
TEXTOM is a great program for text mining without coding. However, many graduate students, researchers, and office workers are wasting time and experiencing stress and burden because they do not know how to actually use Textom even after reading lectures or books.
An instructor with no practical experience and who has never written a paper? I can't really explain how to do "big data analysis."
We will reveal the core know-how and secrets of completing big data analysis in just half a day using Textom .
Never self-study. Text mining has a wide variety of analysis techniques.
If you proceed solely based on theory and manuals without knowing how to use frequently used analysis techniques, you will definitely be wasting your time .
After practicing techniques frequently used in practice or big data papers , you should gradually solidify your theoretical foundation so that you can later analyze texts freely.
In other words, you need to know what's important and what's not in text analysis . Knowing this can save you weeks, or even months, of time.
I have accumulated expertise through hundreds of text analysis experiences. This is a uniquely possible story, as I am a Prime expert, a title given to the top 2% of data analysts on the talent sharing platform (Cremon).
For efficient learning and practical application, we recommend starting Textom with this course.
Big data analysis, now do it yourself. Instead of boring theoretical explanations, we've collected only practical techniques .
The reason why it is difficult to learn text mining is because we only learned the theory without practicing with real-world examples .
You can never analyze something unless you implement it yourself.
To master big data analysis, you need to gain a deep understanding of text mining through hands-on practice using Textom.
We will guide you through extracting the data you need for your research paper.
Through well-defined explanations of core concepts and practical exercises using Textom, we'll guide you to the expert level where you can confidently apply text mining analysis in your work or research papers.
Contains only core techniques that can be applied in practice.
Text mining and big data are increasingly being introduced in all fields of research.
In corporate practice, you also need to be able to handle text data to be recognized.
We've created a Textom lecture focused on practical exercises so that anyone can easily learn the current trends of big data and text mining without complex or difficult coding.
This course is based on accumulated practical know-how, focusing on core text mining techniques used in actual practice, while boldly omitting features that are not used in practice .
✅ Textom data extraction for practical use
✅ How to Use Textom to Write Big Data Papers
🚩 I also went through a lot of trial and error when I first started using Textom.
'How should I use Textom in this situation?' I searched the internet and manuals, but... the Textom explanations and lectures on the market were too theoretical and difficult for a beginner to understand. I remember searching through various menus and struggling for days before finally solving the problem.
When I was a beginner like myself and had difficulty using Textom, I thought, "If only I had someone to guide me, wouldn't it have been easier to analyze big data and write papers? " and "It would be really convenient if there was a lecture that could guide people who are new to Textom or looking for how to use it." With these thoughts in mind, I prepared this lecture.
This course is structured to provide a hands-on demonstration of the data extraction process, rather than a lengthy theoretical explanation of Textom, allowing readers to gain a feel for big data analysis . By following along repeatedly, anyone can utilize big data analysis and text mining techniques without any Python coding experience.
Focused on practical usage This is a text mining lecture.
This lecture focuses on practical use of Textom for text mining rather than theoretical explanations.
The lectures are structured so that you can perform big data analysis, such as trend analysis and perception analysis, by listening to the lectures and following along.
Overview of examples practiced in class
After briefly explaining the basic theory of text mining, we will directly extract big data using the Textom program.
In this course, you will naturally understand text mining and implement big data analysis methods yourself.
In addition, we directly implement SNS big data analysis, which is a current trend.
If you have a basic understanding of text mining and Textom, briefly skim the manual provided by Textom, and then learn the practical methods in this lecture, you will be able to upgrade your text mining analysis skills very quickly.
I recommend this to these people!
Anyone who wants to learn text mining with Textom
For those who want to know about practical implementation methods using Textom rather than theoretical explanations
Graduate students, researchers, professors, etc. who want to write a paper on recognition analysis and trend analysis using Textom
💾 Please check before taking the class!
Securing TEXTOM storage is essential for the course. Before taking the course, create a TEXTOM account and secure at least 10MB of storage with a paid account .
We provide students with a PPT on text mining theory.
Hello, this is Jin-gyu Lee.
Knowledge sharer history
Currently a Ph.D. candidate at Dongguk University's AI Graduate School (Natural Language Processing major)
Natural language processing development at a startup specializing in AI and big data
Former public institution big data analysis researcher
Dankook University and other universities offer Python data analysis and big data analysis courses.
Numerous private tutoring experiences related to data analysis
Kmong AI natural language processing, big data analysis Prime service operation (Kmong's top 2% service)
Experience in writing numerous big data papers and projects using TEXTOM
Q&A 💬
Q. Can I write a paper on research trends or cognitive analysis big data after taking this course?
This course will help those who wish to write a big data paper extract data.
Q. I want to apply text mining to my work. Is it okay to take this course?
Yes, this course consists of practical exercises on analytical methods commonly used in text mining. Those interested in applying these methods in their work are also encouraged to attend.
Q. I'm a beginner in text mining and Textom. Can I still take the course?
Yes, this course is aimed at beginners, but it covers all the analysis methods you can apply in real-world situations. However, if you're a complete beginner, I recommend reading and studying the Textom manual.
💡 I want to help those who are starting out with text mining and textom!
I'll personally implement the Textom usage methods you've been curious about, provide concise, core explanations of text mining, and support you as you consider big data analysis and research. Thank you so much for reading. See you in class!
"If you have any questions after taking the class, please leave them at my email address (leejinkyu0612@naver.com) and I will answer them one-on-one!"
1:1 Coaching Example
Recommended for these people
Who is this course right for?
Learn Text Mining?
Those who don't know coding but want to do big data analysis
Anyone who wants to know how to use Textom
Those who wish to learn Textom through practical examples, not theory
Hello. I am Lee JinKyu (Ph.D. in Engineering, Artificial Intelligence), CEO of Happy AI, who has consistently worked with AI and big data analysis across R&D, education, and project sites.
I have analyzed various unstructured data, such as surveys, documents, reviews, media, policies, and academic data, based on Natural Language Processing (NLP) and text mining. Recently, I have been delivering practical AI application methods tailored to organizations and work environments using Generative AI and Large Language Models (LLMs).
I have collaborated with numerous public institutions, corporations, and educational organizations, including Samsung Electronics, Seoul National University, Offices of Education, Gyeonggi Research Institute, Korea Forest Service, and Korea National Park Service, and have conducted a total of over 200 research and analysis projects across various domains such as healthcare, commerce, ecology, law, economics, and culture.
2024.07 ~ Present CEO of Happy AI, a company specializing in Generative AI and Big Data analysis
Ph.D. in Engineering (Artificial Intelligence) Dongguk University Graduate School of AI
Major: Large Language Models (LLM) (2022.03 ~ 2026.02) 2023 ~ 2025 Public News AI Columnist (Generative AI Bias, RAG, LLM Utilization Issues) 2021 ~ 2023 AI & Big Data Specialist Company Stell
Major: Large Language Models (LLM)
Bio (Summary) 2024.07 ~ Present CEO of Happy AI, a company specializing in Generative AI and Big Data Analysis Ph.D. in Engineering (Artificial Intelligence) Dongguk University Graduate School of AI Major: Large Language Models (LLM)
(March 2022 – February 2026)
2023 ~ 2025 Public News AI Columnist (Generative AI Bias, RAG, LLM Utilization Issues)
2021 ~ 2023 Developer at Stellavision, an AI and Big Data company
2018 ~ 2021 Government-funded Research Institute NLP & Big Data Analysis Researcher
Document Summarization, Classification, and Analysis
Natural Language Processing (NLP) and text mining for reviews, media, policy, and academic data. Topic analysis, sentiment analysis, and keyword networks. Public and corporate AI workflow automation for document summarization, classification, and analysis.
🎒 Courses & Activities (Selected)
2025
LLM/sLLM Application Development (Fine-tuning, RAG, and Agent-based) – KT
2024
LangChain·RAG-based LLM Programming – Samsung SDS
LLM Theory and RAG Chatbot Development Practice – Seoul Digital Foundation
Introduction to Big Data Analysis based on ChatGPT – LetUin Edu
AI Fundamentals & Prompt Engineering Techniques – Korea Vocational Development Institute
LDA & Sentiment Analysis with ChatGPT – Inflearn
Python-based Text Analysis – Seoul National University of Science and Technology
Building LLM Chatbots with LangChain – Inflearn
2023
Python Basics using ChatGPT – Kyonggi University
Big Data Expert Course Special Lecture – Dankook University
Fundamentals of Big Data Analysis – LetUin Edu
💻 Projects (Summary)
Building a Private LLM-based RAG Chatbot (Korea Electric Power Corporation)
LLM-based Big Data Analysis for Forest Restoration (National Institute of Forest Science)
Private LLM Text Mining Solution for Internal Networks (Government Agency)
Instruction Tuning and RLHF-based LLM Model Development
Healthcare, Law, Policy, and Education Data Analysis
AI Analysis of Survey, Review, and Media Data
→ Over 200 projects completed, including public institutions, corporations, and research institutes
📖 Publication (Selected)
Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms (2024)
Improving Generation of Sentiment Commonsense by Bias Mitigation – International Conference on Big Data and Smart Computing (2023)
Analysis of Perceptions of LLM Technology Based on News Big Data (2024)
Numerous NLP-based text mining studies (Forestry, Environment, Society, and Healthcare sectors)
🔹 Others
Python-based data analysis and visualization
Data Analysis Using LLM
Improving work productivity using ChatGPT, LangChain, and Agents
I was looking for a lecture to study for my thesis and came across this lecture. I want to apply it step by step while practicing because it covers practical content. Thank you for the great lecture :D
This is a really effective lecture that not only provides theoretical information but also practical tips. I had fun studying as if I was receiving private tutoring!
I needed to work on Textom while writing a paper, and it was very helpful that you explained everything in detail from theory to practice. There was some repetitive content, but I think I got more familiar with it because I kept hearing it!! Thank you for the great lecture :)