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Text Mining (part 1)  -  Import Text into R (single document)
 
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Text Mining with R. Import a single document into R.
Views: 22116 Jalayer Academy
Analyzing Text Data with R on Windows
 
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Provides introduction to text mining with r on a Windows computer. Text analytics related topics include: - reading txt or csv file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 10750 Bharatendra Rai
Introduction to Text Analytics with R: Overview
 
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The overview of this video series provides an introduction to text analytics as a whole and what is to be expected throughout the instruction. It also includes specific coverage of: – Overview of the spam dataset used throughout the series – Loading the data and initial data cleaning – Some initial data analysis, feature engineering, and data visualization About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz5_y0 Watch the latest video tutorials here: https://hubs.ly/H0hz61V0 See what our past attendees are saying here: https://hubs.ly/H0hz6-S0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 73883 Data Science Dojo
Intro to Text Mining Sentiment Analysis using R-12th March 2016
 
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Analytics Accelerator Program, February 2016-April 2016 batch
Views: 25832 Equiskill Insights LLP
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
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We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 167199 Timothy DAuria
Text Mining (part 5) -  Import a Corpus in R
 
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Import multiple text documents and create a Corpus.
Views: 12431 Jalayer Academy
Extract Structured Data from unstructured Text (Text Mining Using R)
 
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A very basic example: convert unstructured data from text files to structured analyzable format.
Views: 13674 Stat Pharm
Text mining in R and Twitter Sentiment Analytics
 
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- Learn how to Analyse sentiments on anything being said on Twitter - Get your own Twitter developer app key and pull tweets - Understand what is sentiment analytics and text mining - Create impressive word clouds - Map sentiments on any topic and break them into bar graphs
Views: 25680 Equiskill Insights LLP
R PROGRAMMING TEXT MINING TUTORIAL
 
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Learn how to perform text analysis with R Programming through this amazing tutorial! Podcast transcript available here - https://www.superdatascience.com/sds-086-computer-vision/ Natural languages (English, Hindi, Mandarin etc.) are different from programming languages. The semantic or the meaning of a statement depends on the context, tone and a lot of other factors. Unlike programming languages, natural languages are ambiguous. Text mining deals with helping computers understand the “meaning” of the text. Some of the common text mining applications include sentiment analysis e.g if a Tweet about a movie says something positive or not, text classification e.g classifying the mails you get as spam or ham etc. In this tutorial, we’ll learn about text mining and use some R libraries to implement some common text mining techniques. We’ll learn how to do sentiment analysis, how to build word clouds, and how to process your text so that you can do meaningful analysis with it.
Views: 4066 SuperDataScience
Text Mining in R Tutorial: Term Frequency & Word Clouds
 
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This tutorial will show you how to analyze text data in R. Visit https://deltadna.com/blog/text-mining-in-r-for-term-frequency/ for free downloadable sample data to use with this tutorial. Please note that the data source has now changed from 'demo-co.deltacrunch' to 'demo-account.demo-game' Text analysis is the hot new trend in analytics, and with good reason! Text is a huge, mainly untapped source of data, and with Wikipedia alone estimated to contain 2.6 billion English words, there's plenty to analyze. Performing a text analysis will allow you to find out what people are saying about your game in their own words, but in a quantifiable manner. In this tutorial, you will learn how to analyze text data in R, and it give you the tools to do a bespoke analysis on your own.
Views: 68201 deltaDNA
Text Analysis For Call Center Short Demo Using R and Google Speech API
 
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This was from about 25 call recordings at a call center that were transcribed from speech to text using the Google Speech API. Once I transcribed the calls - I converted them into plain text (i.e. rtf to txt) and just dropped them in a folder and then used R to play with the text. Basically these packages and R scripts just take the data- put it in a friendly format and clean it up and then fondle it to bring some very general but meaningful insight. You can use this to see what types of calls you are getting, to find correlations with the calls and groupings with the cluster, and to see words that are most often used as well as toggle the thresholds to make it more strict or loose in finding words used more often/less. I will be sharing my scripts at some point, as they are very basic at the moment. But to reproduce this, get your R studio and the libraries needed and run this script here: https://github.com/thesmarthomeninja/ Under the Text Analysis Repo (not the twitter repo). Keep in mind you need your own text to analyze as I can't share that content. Although there are libraries with books and example text datasets if you google tidytext examples and documents online. Also, I'm working on putting together a blog to share this content or sub-domain off my website: https://www.thesmarthomeninja.com You can follow me on any social platform under the handle- The Smart Home Ninja. Subscribe for more content to come!
Views: 2592 The Smart Home Ninja
R tutorial: The TDM & DTM with text mining
 
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Learn more about text mining with R: https://www.datacamp.com/courses/intro-to-text-mining-bag-of-words With your cleaned corpus, you need to change the data structure for analysis. The foundation of bag of words text mining is either the term document matrix or document term matrix. The term document matrix has each corpus word represented as a row with documents as columns. In this example you simply use the TermDocumentMatrix function on a corpus to create a TDM. The document term matrix is the transposition of the TDM so each document is a row and each word is a column. Once again the aptly named DocumentTermMatrix function creates a matrix with documents as rows shown here. In its simplest form, the matrices contain word frequencies. However, other frequency measures do exist. The qdap package relies on a word frequency matrix. This course doesn’t focus on the word frequency matrix, since it is less popular and can be made from a term document matrix.
Views: 15944 DataCamp
Twitter text mining with R
 
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You may visit my website for video and R codes. http://web.ics.purdue.edu/~jinsuh/analyticspractice-twitter.php
Views: 1230 Jinsuh Lee
R - Twitter Mining with R (part 1)
 
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Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates. Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.
Views: 68102 Jalayer Academy
Text Mining: NGram Word Frequency in R
 
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Using R, you can see what how often words occur in an aggregated data set. It is often used in business for text mining of notes in tickets as well as customer surveys. Using a Corpus and TermDocumentMatrix in R we can organize the data accordingly to extract the most common word combos. Direct File: https://github.com/ProfessorPitch/ProfessorPitch/blob/master/R/NGram%20Wordcloud.R Software Versions: R 3.3.3 Java = jre1.8.0_171 (64 bit) R Packages: library(NLP) library(tm) library(RColorBrewer) library(wordcloud) library(ggplot2) library(data.table) library(rJava) library(RWeka) library(SnowballC)
Views: 6212 ProfessorPitch
Topic modeling with R and tidy data principles
 
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Watch along as I demonstrate how to train a topic model in R using the tidytext and stm packages on a collection of Sherlock Holmes stories. In this video, I'm working in IBM Cloud's Data Science Experience environment. See the code on my blog here: https://juliasilge.com/blog/sherlock-holmes-stm/
Views: 12818 Julia Silge
Analyzing Text Data with R (on Mac)
 
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Provides introduction to text mining with r. Text analytics related topics include: - reading txt file - cleaning of text data - creating term document matrix - making wordcloud and barplots. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 2794 Bharatendra Rai
Text Mining (part 3)  -  Sentiment Analysis and Wordcloud in R (single document)
 
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Sentiment Analysis Implementation and Wordcloud. Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 25818 Jalayer Academy
Whatsapp chat sentiment analysis in R | Sudharsan
 
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Whatsapp Chat Sentiment analysis using R programming! Subscribe to my channel for new and cool tutorials. You can also reach out to me on twitter: https://twitter.com/sudharsan1396 Code for this video: https://github.com/sudharsan13296/Whatsapp-analytics
Text Analysis in R (using Twitter data)
 
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Code on Github: https://github.com/msterkel/text-analysis Twitter API tutorial: https://analytics4all.org/2016/11/16/r-connect-to-twitter-with-r/
Views: 1974 Matthew Sterkel
Text Analytics with R | How to find correlation between words - Data Science Tutorial
 
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In this text analytics with R video I've talked abou how you can find correlation between. words and understand the context behind the entire text and the motive of speaker or writer. This helps understand how one specific important word is related to other words in the entire text and we can limit the correlation also to look at only those words which has either high or low correlation. Text analytics with R,how to find correlation between words in R,data science tutorial,finding correlation between words,finding most frequent terms in the entire text,Finding most frequent words in R,word correlation in R,r Word correlation,Learn Text analytics in R,R Text mining,introduction to text analytics with R,most frequent words script in R,R script to find most frequent words,R script to find correlation between words,R script for Text mining
Text Mining (part 2)  -  Cleaning Text Data in R (single document)
 
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Clean Text of punctuation, digits, stopwords, whitespace, and lowercase.
Views: 21211 Jalayer Academy
Text Analytics With R | How to Connect Facebook with R | Analyzing Facebook in R
 
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In this text analytics with R tutorial, I have talked about how you can connect Facebook with R and then analyze the data related to your facebook account in R or analyze facebook page data in R. Facebook has millions of pages and getting emotions and text from these pages in R can help you understand the mood of people as a marketer. Text analytics with R,how to connect facebook with R,analyzing facebook in R,analyzing facebook with R,facebook text analytics in R,R facebook,facebook data in R,how to connect R with Facebook pages,facebook pages in R,facebook analytics in R,creating facebook dataset in R,process to connect facebook with R,facebook text mining in R,R connection with facebook,r tutorial for facebook connection,r tutorial for beginners,learn R online,R beginner tutorials,Rprg
Image Analysis and Processing with R
 
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Link for R file: https://goo.gl/BXEf7M Provides image or picture analysis and processing with r, and includes, - reading and writing picture file - intensity histogram - combining images - merging images into one picture - image manipulation (brightness, contrast, gamma correction, cropping, color change, flip, flop, rotate, & resize ) - low-pass and high pass filter R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 17472 Bharatendra Rai
Data Science Tutorial | Text analytics with R | Cleaning Data and Creating Document Term Matrix
 
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In this Data Science Tutorial video, I have talked about how you can use the tm package in R. tm package is text mining package in r for doing the text mining. Here in this r Programming tutorial video, we have discussed about how to create corpus of data, clean it and then create document term matrix to study each and every important word from the dataset. In the next video, I'll talk about how to do modeling from this data. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
R - Sentiment Analysis and Wordcloud with R from Twitter Data | Example using Apple Tweets
 
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Provides sentiment analysis and steps for making word clouds with r using tweets about apple obtained from Twitter. Link to R and csv files: https://goo.gl/B5g7G3 https://goo.gl/W9jKcc https://goo.gl/khBpF2 Topics include: - reading data obtained from Twitter in a csv format - cleaning tweets for further analysis - creating term document matrix - making wordcloud, lettercloud, and barplots - sentiment analysis of apple tweets before and after quarterly earnings report R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 20507 Bharatendra Rai
Facebook text analysis on R
 
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For more information, please visit http://web.ics.purdue.edu/~jinsuh/.
Views: 12798 Jinsuh Lee
Text Analytics with R | Sentiment Analysis with R | Part 1 | Basics
 
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In this text analytics with R video, I’ve talked about the basics of sentiments analysis with the help of sentimetr package. sentimentr package is really powerful to evaluate the sentences and give them a number basic on how powerful the sentiment is. Because it provides the numeric value to the sentences, it gives us a lot of flexibility for categorizing numbers to understand people’s emotions. Sentiment analysis is very helpful for making important decisions like policies etc. so that there are less conflicts while rolling out any important decision or policy. Text analytics with R,sentiment analysis with R,sentiment analysis basics in R,analyzing sentiments in R,analysis sentiments,how to analyze sentiment in r,R sentiment analysis,R sentiment analysis tutorial,sentiment analysis example,learn sentiment analysis,learn sentiment analysis,sentiment analysis chart,R Programming tutorial,creating sentiment analysis in R,twitter sentiment analysis with r,sentiment analysis r code,sentiment analysis r project
Social Network Analysis with R | Examples
 
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Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 23380 Bharatendra Rai
Text Analytics-6.1 Importing Text file in R Studio
 
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This video discusses the procedure of importing a Text file in R-Studio. Text file used in this video: https://goo.gl/Jm8kv3
Views: 168 Neeraj Kaushik
Data Science Tutorial | Introduction of Text Analytics in R | R Programming Tutorial
 
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In this Data Science Tutorial videos, I am starting the series of Text mining in R. Text mining is a branch of data mining which specifically look at the mining textual data and found knowledge from it. In this video I've given the overview of text mining along with that started with one of the sample data and provided you couple of R Commands to start grilling the data and find basic knowledge from it by creating histogram and tables to look at the distribution of data in R. Link to the text spam csv file - https://drive.google.com/open?id=0B8jkcc4fRf35c3lRRC1LM3RkV0k
Text Mining in R  Term Frequency & Word Clouds
 
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Text Mining in R Term Frequency & Word Clouds
Views: 4446 finlearn
Introduction to Cluster Analysis with R - an Example
 
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Provides illustration of doing cluster analysis with R. R File: https://goo.gl/BTZ9j7 Machine Learning videos: https://goo.gl/WHHqWP Includes, - Illustrates the process using utilities data - data normalization - hierarchical clustering using dendrogram - use of complete and average linkage - calculation of euclidean distance - silhouette plot - scree plot - nonhierarchical k-means clustering Cluster analysis is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 111614 Bharatendra Rai
Sentiment Analysis of Arabic Text using R
 
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Sentiment Analysis of Arabic Text using R R script used https://app.box.com/s/kf2kkxr7737pfbfvvivzw6k6u9ycea8f Dataset https://app.box.com/s/r55q6k1hnamkoyta3z5sj96i3z5krlyd https://app.box.com/s/i5mmlsex483voetto6up0b9zpch7reor
Views: 2699 Stat Pharm
Twitter Text Analytics using R Studio - Project Mosaic UNC Charlotte
 
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An interview with workshop instructor Ryan Wesslen. Workshop will take place on July 27, 2016 at UNC Charlotte.
Views: 165 Project Mosaic
Getting Tweets, Trends, and User Timeline from Twitter using R
 
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Includes working with r for, - getting tweets from twitter - saving data in a csv file - getting worldwide and local twitter trends - getting user timeline Machine Learning videos: https://goo.gl/WHHqWP R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 24000 Bharatendra Rai
Text Mining (part 7) -  Comparison Wordcloud in R
 
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Create a Wordcloud and Comparison Wordcloud for your Corpus. Create a Term Document Matrix in the process.
Views: 9113 Jalayer Academy
Sentiment Analysis in R | R Tutorial | R Analytics | R Programming | What is R | R language
 
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This tutorial will deep dive into data analysis using 'R' language. By the end of this tutorial you would have learnt to perform Sentiment Analysis of Twitter data using 'R' tool. To learn more about R, click here: http://goo.gl/uHfGbN This tutorial covers the following topics: • What is Sentiment Analysis? • Sentiment Analysis use cases • Sentiment Analysis tools • Hands-On: Sentiment Analysis in R The topics related to ‘R’ language are extensively covered in our ‘Mastering Data Analytics with R’ course. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 46080 edureka!
RQDA 1. Introduction of Qualitative Data Analysis with RQDA
 
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Learn basic level qualitative data analysis with RQDA. If you face difficulty in installing R and R studio watch following clip on youtube: https://youtu.be/aSGujXXLu-U
Views: 2161 Atiq Rehman
Introduction to Text Analytics with R: Our First Model
 
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We are now ready to build our first model in RStudio and to do that, we cover: – Correcting column names derived from tokenization to ensure smooth model training. – Using caret to set up stratified cross validation. – Using the doSNOW package to accelerate caret machine learning training by using multiple CPUs in parallel. – Using caret to train single decision trees on text features and tune the trained model for optimal accuracy. – Evaluating the results of the cross validation process. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD4dF0 Watch the latest video tutorials here: https://hubs.ly/H0hD3PC0 See what our past attendees are saying here: https://hubs.ly/H0hD4fc0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 17313 Data Science Dojo
Data Mining Tool:Rattle R GUI
 
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Link to download R Console: https://cran.r-project.org/
Views: 3409 Chandrakala Badaga
Introduction to Text Analytics with R: Text Analytics Fundamentals
 
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Text analytics fundamentals covers: – The importance of splitting data in to training and test datasets – Stratified sampling of imbalanced data using the caret package – Representing text data for the purposes of machine learning – Introduction to tokenization, stop words, and stemming – The bag-of-words model for text analytics – Text analytics considerations for data pre-processing About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD2VJ0 Watch the latest video tutorials here: https://hubs.ly/H0hD3CP0 See what our past attendees are saying here: https://hubs.ly/H0hD3CZ0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 25003 Data Science Dojo
Text Analytics with R | quanteda Package for text mining | Alternative to tm Package for text mining
 
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In this video I have given you a quick reference to quanteda package which is a package for quantitative analysis for text data and an alternative to tm package. In comparison with tm package, quanteda is simple and faster and have many in built functionalities which is required for text analytics or text mining.
Introduction to Text Analytics with R: TF-IDF
 
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TF-IDF includes specific coverage of: • Discussion of how the document-term frequency matrix representation can be improved: – How to deal with documents of unequal lengths. – What to do about terms that are very common across documents. •Introduction of the mighty term frequency-inverse document frequency (TF-IDF) to implement these improvements: -TF for dealing with documents of unequal lengths. -IDF for dealing with terms that appear frequently across documents. • Implementation of TF-IDF using R functions and applying TF-IDF to document-term frequency matrices. • Data cleaning of matrices post TF-IDF weighting/transformation. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- Learn more about Data Science Dojo here: https://hubs.ly/H0hD4l40 Watch the latest video tutorials here: https://hubs.ly/H0hD4lb0 See what our past attendees are saying here: https://hubs.ly/H0hD3R-0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 20005 Data Science Dojo
Add and Customize Text in Plots with R | R Tutorial 2.10 | MarinStatsLectures
 
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Add and Customize Text in Plots with R: How to add descriptive text (labels) to plots made in R and change the font, location and colour of the text with R. Find the Free Practice Dataset (LungCapData) here: (https://bit.ly/2rOfgEJ) For more Statistics and R Programming Tutorials: (https://goo.gl/4vDQzT); ►► Like to support us? You can Donate (https://bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Thumbs up! Either way We Thank You! In this R video tutorial, we will learn how to enhance our plot in R by adding text and labels to plots and changing the font, location and color of the text with R programming language. This tutorial explains how to use the "text", and "mtext" functions and "adj", "col", "cex" and "font" arguments. We will also see how to add lines to a plot (for example to show the value of the mean in a scatterplot) and manipulating those lines (for example changing the color or thickness of the line) with R programming software. These video tutorials are useful for anyone interested in learning data science and statistics with R programming language using RStudio. ► ► Watch More: ► Intro to Statistics Course: https://bit.ly/2SQOxDH ►Data Science with R https://bit.ly/1A1Pixc ►Getting Started with R (Series 1): https://bit.ly/2PkTneg ►Graphs and Descriptive Statistics in R (Series 2): https://bit.ly/2PkTneg ►Probability distributions in R (Series 3): https://bit.ly/2AT3wpI ►Bivariate analysis in R (Series 4): https://bit.ly/2SXvcRi ►Linear Regression in R (Series 5): https://bit.ly/1iytAtm ►ANOVA Concept and with R https://bit.ly/2zBwjgL ►Hypothesis Testing: https://bit.ly/2Ff3J9e ►Linear Regression Concept and with R Lectures https://bit.ly/2z8fXg1 Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support some courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
How is R used in Analytics Industry - Case Study on Text Mining, Regression, & More
 
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To learn predictive analytics in R using case studies, call Ivy at 9748441111 / [email protected] / www.ivyproschool.com/landing Learn R using a case study on Text Mining. How is R used in Analytics Industry? - Learn Basic Text Mining, Regression, & More
Views: 7495 IvyProSchool
Random Forest in R - Classification and Prediction Example with Definition & Steps
 
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Provides steps for applying random forest to do classification and prediction. R code file: https://goo.gl/AP3LeZ Data: https://goo.gl/C9emgB Machine Learning videos: https://goo.gl/WHHqWP Includes, - random forest model - why and when it is used - benefits & steps - number of trees, ntree - number of variables tried at each step, mtry - data partitioning - prediction and confusion matrix - accuracy and sensitivity - randomForest & caret packages - bootstrap samples and out of bag (oob) error - oob error rate - tune random forest using mtry - no. of nodes for the trees in the forest - variable importance - mean decrease accuracy & gini - variables used - partial dependence plot - extract single tree from the forest - multi-dimensional scaling plot of proximity matrix - detailed example with cardiotocographic or ctg data random forest is an important tool related to analyzing big data or working in data science field. Deep Learning: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 66342 Bharatendra Rai
Data Mining with R & RStudio - KMeans Clustering and Visualization
 
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Simple overview of data mining with R and RStudio.
Views: 3331 Gaurav Jetley