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[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.

(4.7) 9 reviews

178 learners

Level Beginner

Course period 4 months

Big Data
Big Data
Text Mining
Text Mining
TEXTOM
TEXTOM
Big Data
Big Data
Text Mining
Text Mining
TEXTOM
TEXTOM

Reviews from Early Learners

4.7

5.0

Jieun So

22% enrolled

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!

This course is for Textom beginners and novices.

I am teaching the basics .

After previewing

If this is what you want, please take the class!

(Note: Intermediate level lecture)

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

Need to know before starting?

  • Basic Knowledge about Text Mining

  • New to TexTOM? Read the official TexTOM manual!

Hello
This is HappyAI

5,640

Learners

347

Reviews

54

Answers

4.5

Rating

12

Courses

Lee JinKyu | Lee JinKyu

Ph.D. in AI Engineering · Adjunct Professor in the Department of AI Software · CEO of Happy AI

Hello.
I am Lee JinKyu, and I research, develop, and teach AI, focusing on natural language processing (NLP) and large language models (LLMs).

I earned a Ph.D. in Engineering specializing in Artificial Intelligence and have conducted research primarily in natural language processing and LLMs.

I am currently an adjunct professor in the Department of AI Software, teaching AI-related courses such as natural language processing and computer vision, and I run HappyAI, a generative AI company.

After working as a natural language processing and big data analysis researcher at a government-funded research institute, and then as a developer at an AI-specialized company, I currently carry out AI projects and training for businesses and public institutions.

At various companies and institutions, including Samsung SDS, KT, Hyundai Wia, and the Seoul Digital Foundation, I have conducted training and projects related to generative AI, LLMs, RAG, AI agents, and fine-tuning.

Additionally, I have conducted natural language processing and text mining research on various types of unstructured data, including documents, surveys, reviews, news media, policy, and academic data, and have experience with research and analysis projects across fields such as healthcare, policy, the environment, law, economics, and education.


Key Career History

  • Adjunct Professor in the Department of AI Software

    • Natural Language Processing

    • Computer Vision

    • Generative AI

    • Data Mining

  • Ph.D. in Engineering (Artificial Intelligence)

     

    • Large Language Model (LLM) and Agent Research

  • CEO of Happy AI

    • Generative AI, LLM, RAG, and AI Agent R&D and education

  • Researcher at a government-funded research institute

    • Natural Language Processing · Big Data Analytics

  • Developer at an AI and Big Data Specialist Company

  • AI Columnist

    • Articles on generative AI, LLMs, RAG, and AI technologies


Key Areas of Expertise

  • Generative AI and large language models (LLMs)

  • RAG · Private LLM

  • AI Agent · Agentic AI

  • AI applications based on LangChain and LangGraph

  • LLM fine-tuning based on LoRA·QLoRA

  • Natural Language Processing (NLP) · Text Mining

  • AI-based data analysis and work automation


Training for Major Companies and Institutions

  • Samsung SDS – LangChain·RAG-based LLM programming

  • KT – LLM/sLLM application development

  • Seoul Digital Foundation – LLM theory and RAG chatbot development

  • Hyundai Wia – Generative AI·AX training

  • Seoul National University of Science and Technology – Python-based text analysis

  • Kyonggi University – Python Using ChatGPT

  • Dankook University – Big Data Expert Program


Key AI Projects

  • Private LLM-based RAG document search and chatbot development

  • Development of Private LLM solutions in an internal network environment

  • LLM Fine-Tuning and Instruction Tuning

  • AI Agent-based business process design

  • Natural language processing and text mining–based research and analysis

  • AI analysis of survey, review, media, policy, and academic data


Research and Publications

  • Conducted domestic and international academic research on natural language processing and LLMs

  • Research on measuring and mitigating LLM bias

  • Numerous papers in the fields of NLP and text mining

  • Experience in AI-related patents and software research and development

  • Book: “Stock Data Analysis with ChatGPT”


What I Consider Most Important in My Lectures

AI technology is changing rapidly, but if you understand the core principles, you can learn new technologies much faster.

In the lectures, rather than simply following features or code,

“what it is → why it is needed → how it works → where it is used in practice”

I consider it most important to explain things clearly so that you can understand them.

Rather than explaining complex AI technologies in a difficult way,
I will explain them so that even beginners can understand the overall structure and apply them to real-world work and projects.


Inquiries about lectures and projects

Email
leejinkyu0612@naver.com

Homepage
https://happyaidata.kr

YouTube
https://www.youtube.com/@HappyAI_0612

GitHub
https://github.com/leejin-kyu

※ Kmong Prime Expert

 

Detailed profile
https://bit.ly/jinkyu-profile

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Curriculum

All

78 lectures ∙ (9hr 38min)

Course Materials:

Lecture resources
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Reviews

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9 reviews

4.7

9 reviews

  • skyplane10222554님의 프로필 이미지
    skyplane10222554

    Reviews 1

    ∙

    Average Rating 5.0

    5

    99% enrolled

    This was very helpful!!

    • benign299848님의 프로필 이미지
      benign299848

      Reviews 3

      ∙

      Average Rating 5.0

      5

      99% enrolled

      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 :)

      • onofone님의 프로필 이미지
        onofone

        Reviews 1

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        Average Rating 5.0

        5

        31% enrolled

        I like it

        • hrjoo10309599님의 프로필 이미지
          hrjoo10309599

          Reviews 1

          ∙

          Average Rating 5.0

          5

          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!

          • luckyii7036823님의 프로필 이미지
            luckyii7036823

            Reviews 1

            ∙

            Average Rating 5.0

            5

            22% enrolled

            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

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