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Data Science

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Data Analysis

Big Data/Text Mining Analysis Methods (LDA, BERTtopic, Sentiment Analysis, CONCOR with ChatGPT)

This is a lecture on learning text mining analysis techniques, big data analysis techniques, word frequency analysis, word cloud visualization, morphological analysis, and topic modeling analysis techniques utilizing Python and ChatGPT, covering how to utilize text mining data analysis techniques necessary for writing papers and basic application methods for research papers.

(4.5) 15 reviews

154 learners

Level Basic

Course period 8 months

  • HappyAI
Big Data
Big Data
Text Mining
Text Mining
Big Data
Big Data
Text Mining
Text Mining

Reviews from Early Learners

Reviews from Early Learners

4.5

5.0

뚱이 언니

63% enrolled

Hello, I am leaving a lecture review after listening to half of the class. I have a PhD in language and have absolutely no knowledge of Python, but I am listening to the lecture with satisfaction because the teacher writes the code well and explains it well. Recently, I kept getting errors related to using LDAvis, so the teacher helped me remotely at 10 PM, but I couldn't do it even after 12:30 PM. It was late, and the teacher was really trying to help me, but I was really sorry that it didn't work, but the teacher corrected the error and sent me the code by email the next day. That's how sincere and passionate the teacher is in teaching. (When I came in, I saw that the teacher had updated the code in the lecture list.) Even if you have absolutely no knowledge of Python like me, you can listen to the lecture, and if you ask the teacher for help, he will help you, so I hope you listen to the lecture. After I finish this course, I plan to listen to the teacher's other (paid) lectures. I sincerely recommend the lecture content and the teacher.

5.0

밝은 비버

45% enrolled

This is a lecture that I found like a ray of light when I was frustrated while studying text mining on my own :-) Why is it so easy to understand when it explains all the hot methodologies that are often covered in theses these days..!! (Honestly, if I were to take it at school, it would be a 1-semester lecture..How come you explain it so compactly?>.<) More than anything, it was a practical lecture, not a theory lecture, so it was very helpful in practice, and it was a lecture that completely condensed knowledge, so I didn't regret the tuition at all. In fact, it felt cheap..ㅠㅠ One more thing that is the most...perfect thing about this lecture..!! When I leave a question, the teacher gives feedback almost in real time, and tries to solve it together until the end, so I dare to give a thumbs up!!^^bb Thank you, teacher~~

5.0

우아한 북극곰

100% enrolled

This lecture is truly a blessing in disguise for graduate students or researchers preparing a text mining research paper. There are many books on Python text mining on the market, but there is no one that provides guidance for beginners in coding to actually apply coding to research papers. This lecture is very helpful because it shows the entire process of how Python coding is actually done from the data collection stage to the analysis method so that it can be applied directly to research papers. I especially liked the fact that they gave me quick and friendly feedback on parts I was curious about. This lecture is not a lecture that explains the principles of coding grammar in detail. However, since it is a lecture that allows you to immediately apply the necessary parts of your research to real-life situations even if you don't know how to code, it is a lecture that is perfect for those who want to do text mining research but have difficulty applying coding!

What you will gain after the course

  • Summary of Key Tips for Text Mining using Python

  • Word frequency analysis

  • Word Cloud Visualization

  • Morphological analysis

  • TF-IDF for identifying importance in text

  • Text Topic Classification using the LDA Method

  • Data Interpretation Methods for Big Data/Text Mining Thesis Writing

  • Explanation of the core theory of Text Mining

  • Sentiment Analysis using the KNU Sentiment Dictionary (Sentiment Word Extraction, Sentiment Document Ratio)

For text mining or writing a paper related to text mining
Welcome to those who have concerns! 🙌

It will be easier to understand if you take the free lectures "Basic Text Mining: App Review Analysis with Python" and "Textom Basics Lecture: SNS Recognition Analysis for Writing Big Data Papers" before taking the course .

This lecture guides you through writing papers using core techniques used in text mining/big data analysis papers, from classical models such as LDA topic modeling and KNU sentiment analysis to the latest technique, BERTopic.

This lecture is designed to help graduate students and researchers in the humanities, arts, physical education, health, and medicine fields write big data papers.

Non-majors and liberal arts students can eliminate their fear and uncertainty about writing text mining papers through this lecture !

The lecture also includes know-how on how to easily perform big data/text mining analysis using ChatGPT .

" 📖 Beginners don't need to waste hours analyzing big data papers/texts. "

"A lecture for those who want concise but deep learning"

📚 This is not a lecture that explains complex theories or lengthy, theoretical explanations.

Mathematical theory used in LDA model

🗝 This is not a lecture that provides a theoretical explanation at the level of experts, but a practical lecture that can be immediately applied by those writing papers on work or big data.

Visualization results of LDA models mainly used in papers and practice

We will also guide you on how to use the BERTopic model, which has been appearing a lot recently.

🗝 If you listen to the lecture and change only the code used, you can apply text analysis . (Code that enables data analysis by changing only the input data is provided)

📚 This lecture is a condensed version of 6 years of text analysis experience and research.

📚 We teach you the most used core techniques for beginners.

Text mining and big data are increasingly being introduced in all research fields.

Corporate practice also requires the ability to handle text data to be recognized.

We have created an efficient lecture so that anyone can easily follow the current trend of big data and text mining instead of complex and difficult coding.

  • 📚 Text analysis projects and research using Python

      

    💻 This lecture will help you write a big data research paper using text mining techniques! (This is an introductory lecture for beginners.)

    🚀 We will teach you the core theories of text analysis and text analysis techniques that can be used in practice.

    🗝 We help you discover insights from large amounts of text documents.

  • ✅ This course teaches big data analysis and text mining analysis techniques using Python, and covers how to extract meaningful information from data and text.

  • ✅ This is a text analysis lecture for writing big data papers (the most basic lecture for recognition analysis and trend analysis).

  • Learn about data collection and refinement, text data preprocessing, frequency analysis, TF-IDF analysis, word cloud, LDA, and topic modeling using sentiment analysis.

✅ We will guide you on how to do big data analysis more easily by using ChatGPT , the latest LLM.

Learn things like this 📚

You will understand how to analyze big data and text data using Python.

You will learn techniques required for data analysis, such as data preprocessing, visualization, and statistical analysis .

You can learn the data analysis skills required to write a big data paper.

You can improve your data analysis skills by learning various data and text analysis tools and techniques .

We will practice the process of collecting BigKinds data and directly extracting data to be used in papers.

We will guide you on how to make big data analysis easier by utilizing ChatGPT, an AI model that is currently trending.


I recommend this to these people 🙆‍♀️

Researchers, data scientists, and machine learning engineers working in the data field

Researchers and graduate students who want to write papers on text mining and big data

Anyone interested in big data analysis and text mining analysis technology

Join this lecture 😊

Students who are anxious about data analysis and paper writing can strengthen their skills through this course.

By learning how to extract meaningful information from data and text through lectures, students will be able to acquire high-level capabilities in the fields of big data analysis and text mining. These capabilities will greatly contribute to the enhancement of students' job competency and academic achievement.


Lecture Features ✨

Provides information on data extraction and analysis required for paper writing

Practical data analysis using real Python

Easy explanation that even Python beginners can follow

Learn how to extract meaningful information from big data and text data with Python.

The right ratio of theory and practice! Practice in which actual data analysis is performed based on theory.

Please write a course review after completing 100% of the course . We will provide Textom lectures to those who complete 100% of the course and write a review!


Recommended for
these people

Who is this course right for?

  • Graduate students, researchers aiming to write Text Mining/Big Data papers

  • Those who want to learn text mining techniques

  • Those interested in text mining using Python

  • Conducting Text Mining Projects and Research Tasks

  • Those who want SNS data analysis

  • Those who want to understand customer needs for marketing by analyzing customer feedback and reviews

  • Those who want to try sentiment analysis

Need to know before starting?

  • You need some knowledge of Python basics.

  • Need to know how to use Google Colab

Hello
This is

4,481

Learners

224

Reviews

51

Answers

4.6

Rating

11

Courses

Lee JinKyu | Lee JinKyu

AI·LLM·Big Data Analysis Expert / CEO of Happy AI

👉You can check the detailed profile at the link below.
https://bit.ly/jinkyu-profile

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.


🎒 Inquiries for Lectures and Outsourcing

Kmong Prime Expert (Top 2%)


📘 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) (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


🔹 Areas of Expertise (Lecture & Project Focused)

  • Generative AI and LLM Utilization

    • Private LLM, RAG, Agent

    • Basics of LoRA and QLoRA Fine-tuning

  • AI-based Big Data Analysis

    • Survey, review, media, policy, and academic data

  • Natural Language Processing (NLP) & Text Mining

    • Topic Analysis, Sentiment Analysis, Keyword Networks

  • Public/Corporate AI Task Automation

    • 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

Curriculum

All

48 lectures ∙ (5hr 38min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

15 reviews

4.5

15 reviews

  • runying03057863님의 프로필 이미지
    runying03057863

    Reviews 1

    Average Rating 5.0

    5

    63% enrolled

    Hello, I am leaving a lecture review after listening to half of the class. I have a PhD in language and have absolutely no knowledge of Python, but I am listening to the lecture with satisfaction because the teacher writes the code well and explains it well. Recently, I kept getting errors related to using LDAvis, so the teacher helped me remotely at 10 PM, but I couldn't do it even after 12:30 PM. It was late, and the teacher was really trying to help me, but I was really sorry that it didn't work, but the teacher corrected the error and sent me the code by email the next day. That's how sincere and passionate the teacher is in teaching. (When I came in, I saw that the teacher had updated the code in the lecture list.) Even if you have absolutely no knowledge of Python like me, you can listen to the lecture, and if you ask the teacher for help, he will help you, so I hope you listen to the lecture. After I finish this course, I plan to listen to the teacher's other (paid) lectures. I sincerely recommend the lecture content and the teacher.

    • 밝은 비버님의 프로필 이미지
      밝은 비버

      Reviews 1

      Average Rating 5.0

      5

      45% enrolled

      This is a lecture that I found like a ray of light when I was frustrated while studying text mining on my own :-) Why is it so easy to understand when it explains all the hot methodologies that are often covered in theses these days..!! (Honestly, if I were to take it at school, it would be a 1-semester lecture..How come you explain it so compactly?>.<) More than anything, it was a practical lecture, not a theory lecture, so it was very helpful in practice, and it was a lecture that completely condensed knowledge, so I didn't regret the tuition at all. In fact, it felt cheap..ㅠㅠ One more thing that is the most...perfect thing about this lecture..!! When I leave a question, the teacher gives feedback almost in real time, and tries to solve it together until the end, so I dare to give a thumbs up!!^^bb Thank you, teacher~~

      • yeobi852767님의 프로필 이미지
        yeobi852767

        Reviews 1

        Average Rating 5.0

        5

        100% enrolled

        This lecture is truly a blessing in disguise for graduate students or researchers preparing a text mining research paper. There are many books on Python text mining on the market, but there is no one that provides guidance for beginners in coding to actually apply coding to research papers. This lecture is very helpful because it shows the entire process of how Python coding is actually done from the data collection stage to the analysis method so that it can be applied directly to research papers. I especially liked the fact that they gave me quick and friendly feedback on parts I was curious about. This lecture is not a lecture that explains the principles of coding grammar in detail. However, since it is a lecture that allows you to immediately apply the necessary parts of your research to real-life situations even if you don't know how to code, it is a lecture that is perfect for those who want to do text mining research but have difficulty applying coding!

        • pupplejin님의 프로필 이미지
          pupplejin

          Reviews 1

          Average Rating 5.0

          5

          100% enrolled

          • aa47474198510님의 프로필 이미지
            aa47474198510

            Reviews 1

            Average Rating 5.0

            5

            100% enrolled

            It was great that you showed us how to apply this to research papers too! I hope there will be more advanced lectures like this in the future :)

            $84.70

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