
Building the Basics of R Programming
coco
This course covers the basics of R programming for those who have no knowledge of R programming.
입문
R
This is a lecture that will teach you step-by-step how to use R to analyze top keywords, wordclouds, sentiment analysis, and topic modeling in text.
302 learners
Level Basic
Course period Unlimited

Reviews from Early Learners
5.0
Hyejin Kwon
Actually, it's good to do it while practicing together, but it was difficult to follow along at the same time when I was still immature. But I think it would be good to watch it repeatedly. I think it was an important lecture because there aren't many places where you can listen to Korean text analysis, especially topic and sentiment analysis, online. I think I'll understand it a little better and be able to use the code directly if I watch it one more time. And it was really good that they gave me all the code in advance as materials.
5.0
아쿠아라이드
As someone who studies R machine learning and applies it to my work, I think this is the best cost-effective lecture that can help me understand not only text mining but also the overall machine learning concept, and improve my R coding skills, and that can simultaneously capture both practical applicability and skill improvement. The last lecture, LDA, was a bit difficult, but... If I ever need a similar analysis later, I think I can watch the lecture again and apply it~~ I would like to express my gratitude to the instructor for providing such a high-quality lecture at a very low price... I will see you again in the next lecture. ㅎㅎ
5.0
DT로
I also analyzed keywords while performing reports and work and organized them into important keywords. It was a good class to learn about the numerical factors of machine learning through sentiment analysis. I recommend it.
How to do morphological analysis
Top keyword extraction
How to draw a pretty wordcloud
Sentiment analysis
Topic Modeling(LDA)
In the sea of pouring data
Let's create some gem-like information! 💎
Text mining is a process of mining unstructured data. Mining involves extracting statistically significant concepts and extracting information through patterns within them. Text mining refers to mining unstructured data such as videos, messages, and location information . However, unstructured data lacks a defined format, making data collection difficult.
In this lecture, you'll learn how to handle unstructured data, which is becoming increasingly important due to the rise of social media!
Learn about KoNLP, a morphological analyzer, extract top keywords, and create a word cloud.
Learn how to build your own dictionary to perform sentiment analysis, and how to do it using regression analysis (machine learning).
Sentiment analysis is a process of quantifying subjective information, such as emotions or opinions, contained in text, based on words and context . Sentiment analysis is also actively used in business, such as gathering and leveraging consumer opinions on products and services. Sentiment analysis is also a type of text mining technology.
Q. Can non-majors also take the course?
This course assumes a basic understanding of the R language . Those who have taken the "Introduction to Web Crawling with R" course will find this course easy to follow.
Who is this course right for?
Anyone who wants to do text mining with R
Anyone who has taken the crawling class
8,388
Learners
509
Reviews
136
Answers
4.4
Rating
20
Courses
I am an unemployed scholar who majored in statistics as an undergraduate, earned a PhD in industrial engineering (artificial intelligence), and is still studying.
Awards ㆍ 6th Big Contest: Game User Churn Algorithm Development / NCSOFT Award (2018) ㆍ 5th Big Contest: Loan Delinquency Prediction Algorithm Development / Korea Association for ICT Promotion
Awards
ㆍ 6th Big Contest Game User Churn Prediction Algorithm Development / NCSOFT Award (2018)
ㆍ 5th Big Contest Loan Defaulter Prediction Algorithm Development / Korea Association for ICT Promotion (KAIT) Award (2017)
ㆍ 2016 Weather Big Data Contest / Korea Institute of Geoscience and Mineral Resources President's Award (2016)
ㆍ 4th Big Contest: Development of Insurance Fraud Prediction Algorithm / Finalist (2016)
ㆍ 3rd Big Contest Baseball Game Prediction Algorithm Development / Minister of Science, ICT and Future Planning Award (2015)
* blog : https://bluediary8.tistory.com
My primary research areas are data science, reinforcement learning, and deep learning.
I am currently doing crawling and text mining as a hobby :)
I developed an app called Marong that uses crawling to collect and display only popular community posts,
I also created a restaurant recommendation app by collecting lists of famous restaurants and blog posts from across the country :) (it failed miserably..)
I am currently a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting blog posts and lists of top-rated restaurants across the country :) (though it failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
I even developed a restaurant recommendation app by collecting lists of famous restaurants and blogs from all over the country :) (It failed miserably...) Now, I am a PhD student researching artificial intelligence.
All
13 lectures ∙ (4hr 10min)
Course Materials:
1. Orientation
06:06
All
11 reviews
4.3
11 reviews
Reviews 3
∙
Average Rating 5.0
5
Actually, it's good to do it while practicing together, but it was difficult to follow along at the same time when I was still immature. But I think it would be good to watch it repeatedly. I think it was an important lecture because there aren't many places where you can listen to Korean text analysis, especially topic and sentiment analysis, online. I think I'll understand it a little better and be able to use the code directly if I watch it one more time. And it was really good that they gave me all the code in advance as materials.
Reviews 12
∙
Average Rating 5.0
5
As someone who studies R machine learning and applies it to my work, I think this is the best cost-effective lecture that can help me understand not only text mining but also the overall machine learning concept, and improve my R coding skills, and that can simultaneously capture both practical applicability and skill improvement. The last lecture, LDA, was a bit difficult, but... If I ever need a similar analysis later, I think I can watch the lecture again and apply it~~ I would like to express my gratitude to the instructor for providing such a high-quality lecture at a very low price... I will see you again in the next lecture. ㅎㅎ
Reviews 4
∙
Average Rating 5.0
Reviews 10
∙
Average Rating 5.0
5
I have already signed up for and am listening to 4 of the instructor's lectures. It is very helpful for understanding text mining and sentiment analysis in R. However, the only regret is that I am distracted because I have to copy the code while coding and go back and forth between the execution screens. It would be better if the instructor explained it after editing it. ^^
Reviews 3
∙
Average Rating 5.0
5
This is such a great lecture!! I want to listen to Coco's lecture again. I definitely want to listen to the next lecture too.
Thank you always for the great review. I hope it helps :)
$34.10
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