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Introduction to Text Analytics with R: SVD with R
 
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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 Part 8 of this video series includes specific coverage of: - Use of the irlba package to perform truncated SVD. - How to project a TF-IDF document vector into the SVD semantic space (i.e., LSA). - Comparison of model performance between a single decision tree and the mighty random forest. - Exploration of random forest tuning using the caret package. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2tVeF68 See what our past attendees are saying here: http://bit.ly/2ty65Ip -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 6214 Data Science Dojo
Text Mining in JMP with R
 
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Some estimates suggest that unstructured text accounts for roughly 80 percent of the information stored by most organizations. This presentation by Andrew T. Karl, Senior Management Consultant at Adsurgo LLC, and Heath Rushing, Principal Consultant and Co-Founder of Adsurgo LLC, provides an overview of methods easily implemented with the R interface to JMP to find previously unknown relationships from a collection of unstructured data. By utilizing R packages for text mining and sparse matrix algebra, JMP may be equipped to extract information from text without requiring end-user knowledge of R. The text -- which may be from emails, survey comments, social media, incident reports, insurance claim reports, etc. -- may be used for several purposes. Vectors from a singular value decomposition of the document term matrix produced in R may be added to the original data table in JMP and included in predictive models (e.g., via the Fit Model or Neural platforms) or clustering algorithms (via the Cluster platform). Another goal may be to explore the underlying themes of the text though word counts or latent semantic indexing. We will demonstrate a JSL/R script that provides such functionality. This presentation was recorded at Discovery Summit 2013 in San Antonio, Texas.
Views: 5371 JMPSoftwareFromSAS
Introduction to Text Analytics with R: VSM, LSA, & SVD
 
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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 Part 7 of this video series includes specific coverage of: - The trade-offs of expanding the text analytics feature space with n-grams. - How bag-of-words representations map to the vector space model (VSM). - Usage of the dot product between document vectors as a proxy for correlation. - Latent semantic analysis (LSA) as a means to address the curse of dimensionality in text analytics. - How LSA is implemented using singular value decomposition (SVD). - Mapping new data into the lower dimensional SVD space. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2tS79Jq See what our past attendees are saying here: http://bit.ly/2svl84m -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 7900 Data Science Dojo
Introduction to Text Analytics with R: Overview
 
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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 is 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 Part 1 of this video series provides an introduction to the video series and includes specific coverage: - 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 Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam-collection-dataset The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2rtEB7n See what our past attendees are saying here: http://bit.ly/2rtmZbK -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 50504 Data Science Dojo
Introduction to Text Analytics with R: TF-IDF
 
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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 Part 5 of this video series 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. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2tFV5HX See what our past attendees are saying here: http://bit.ly/2tG3uLg -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 12224 Data Science Dojo
Lecture 47 — Singular Value Decomposition | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
DataMining12-L15 : SVD (1 of 3)
 
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Video Lectures by Prof. Jeff M. Phillips given as courses in the School of Computing at the University of Utah. Topics include Data Mining, Computational Geometry, and Big Data Algorithmics.
Views: 210 Jeff Phillips
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: 1685 SuperDataScience
How to run the text mining (tm) package in R
 
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Link to the article http://goo.gl/w24W2 . Link to the script http://goo.gl/gpUYR
Views: 16955 resinnovstation
Dimension Reduction Part 2
 
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This is the second part of the third module in the 2016 Exploratory Analysis of Biological Data Using R workshop hosted by the Canadian Bioinformatics Workshops. This lecture is by Boris Steipe from the University of Toronto. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/
Introduction to Text Analytics with R: Text Analytics Fundamentals
 
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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 Part 2 of this video series includes specific coverage of: - 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 The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2rDqQkN See what our past attendees are saying here: http://bit.ly/2qYmQtf -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 17031 Data Science Dojo
SAS TextCluster
 
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How to use Text Cluster in SAS Enterprise Miner?
Views: 634 Dothang Truong
Text Sentiment With Exploratory, An R-based Data Tool (Ep 039)
 
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In this episode, Kevin shows how to wrangle the 2016 general election debate data using Exploratory, an R-based data wrangling and visualization tool (http://exploratory.io), and add text sentiment using the tool's built-in R function. Created using Exploratory 2.5.1.3. http://redpillanalytics.com http://redpillanalytics.com/dataviz-daily/
Views: 257 Red Pill Analytics
Introduction to Text Analytics with R: Data Pipelines
 
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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 Part 3 of this video series provides an introduction to the video series and includes specific coverage: - Exploration of textual data for pre-processing “gotchas” - Using the quanteda package for text analytics - Creation of a prototypical text analytics pre-processing pipeline, including (but not limited to): tokenization, lower casing, stop word removal, and stemming. - Creation of a document-frequency matrix used to train machine learning models Kaggle Dataset: https://www.kaggle.com/uciml/sms-spam... The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2ryJhZP See what our past attendees are saying here: http://bit.ly/2rSbZoX -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 13359 Data Science Dojo
Text Processing in R by Tim Hoolihan (5/24/2017)
 
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Tim Hoolihan presents on working with text in R using the following packages: tm, topicmodels, lsa.
Active Learning with SAS®  Text Miner
 
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The video illustrates key enhancements with the 12.1 release of SAS Text Miner.
Views: 14739 SAS Software
Introduction to Text Analytics with R: Our First Model
 
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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 Part 4 of this video series includes specific coverage of: - 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. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2s7hrnH See what our past attendees are saying here: http://bit.ly/2sFalYG -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 12149 Data Science Dojo
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: 12967 Jalayer Academy
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
SVD for Compression Demo (R)
 
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Demonstration of how to use SVD to compress images BitBucket: https://bitbucket.org/byrnesj1/project-la/src
Views: 208 Jeffrey Byrnes
Introduction to Text Analytics with R: Your First Test
 
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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 Part 11 of this video series includes specific coverage of: - Pre-processing new, unseen textual data to allow for predictions from our trained model. - The importance of caching the IDF values calculated from the training data set to TF-IDF new, unseen, pre-processed data. - Performing SVD projections of new, unseen, pre-processed textual data into the latent semantic space. - Creating predictions and evaluating model effectiveness in the context of accuracy, sensitivity, and specificity. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2wfFKj6 See what our past attendees are saying here: http://bit.ly/2v4JbuA -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 3238 Data Science Dojo
Text Mining (part 8) -  Sentiment Analysis on Corpus in R
 
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Sentiment Analysis Implementation Find the terms here: http://ptrckprry.com/course/ssd/data/positive-words.txt http://ptrckprry.com/course/ssd/data/negative-words.txt
Views: 4946 Jalayer Academy
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: 61272 Jalayer Academy
Perceptual mapping in R JSM shiny app
 
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Video to demo how-to use for the shiny app for JSM perceptual mapping in R. for my business and analytics students
Views: 373 Sudhir Voleti
Introduction to Text Analytics with R: Model Metrics
 
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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 Part 9 of this video series includes specific coverage of: - The importance of metrics beyond accuracy for building effective models. - Coverage of sensitivity and specificity and their importance for building effective binary classification models. - The importance of feature engineering for building the most effective models. - How to identify if an engineered feature is likely to be effective in Production. - Improving our model with an engineered feature. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2syHvpk See what our past attendees are saying here: http://bit.ly/2u0GGtY -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 3533 Data Science Dojo
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: 5949 Julia Silge
Text Analysis of Harkive stories using R
 
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Video overview of Text Analysis with R. See http://www.harkive.org/h17-text-analysis for more information, sample data and script.
Views: 366 Harkive
Introduction to Text Analytics with R: Cosine Similarity
 
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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 Part 10 of this video series includes specific coverage of: - How cosine similarity is used to measure similarity between documents in vector space. - The mathematics behind cosine similarity. - Using cosine similarity in text analytics feature engineering. - Evaluation of the effectiveness of the cosine similarity feature. The data and R code used in this series is available via the public GitHub: https://github.com/datasciencedojo/In... -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3200+ employees from over 600 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: http://bit.ly/2vpFuRi See what our past attendees are saying here: http://bit.ly/2us4v9H -- Like Us: https://www.facebook.com/datascienced... Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data... Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 5798 Data Science Dojo
Computational Linear Algebra 2: Topic Modelling with SVD & NMF
 
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Course materials available here: https://github.com/fastai/numerical-linear-algebra We use a dataset of messages posted on discussion forums to identify topics. A term-document matrix represents the frequency of the vocabulary in the documents. We factor it using Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF). We use PyTorch as a GPU-accelerated alternative to Numpy to speed things up, and we cover Stochastic Gradient Descent, a very useful, general purpose optimization algorithm. This video is fast-paced, so be sure to watch Lesson 3 for a review and Q&A of the topics covered here. Course overview blog post: http://www.fast.ai/2017/07/17/num-lin-alg/ Taught in the University of San Francisco MS in Analytics (MSAN) graduate program: https://www.usfca.edu/arts-sciences/graduate-programs/analytics Ask questions about the course on our fast.ai forums: http://forums.fast.ai/c/lin-alg Topics covered: - Singular Value Decomposition (SVD) - Non-negative Matrix Factorization (NMF) - Stochastic Gradient Descent (SGD) - Intro to PyTorch
Views: 8670 Rachel Thomas
Document Similarity and Clustering in RapidMiner
 
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This is part 4 of a 5 part video series on Text Mining using the free and open-source RapidMiner. This video describes how to calculate a term's TF-IDF score, as well as how to find similar documents using cosine similarity, and how to cluster documents using the K-Means algorithm.
Views: 47251 el chief
Principal Component Analysis and Singular value Decomposition in Python - Tutorial 19 in Jupyter
 
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In this python for data science tutorial, you will learn about how to do principal component analysis (PCA) and Singular value decomposition (SVD) in python using seaborn, pandas, numpy and pylab. environment used is Jupyter notebook. This is the 19th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets
Views: 7583 TheEngineeringWorld
text analytics 7
 
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Bag of words in R
Views: 21 litpuvn
Text Analysis Part 1
 
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Views: 57 Tim H
SKlearn PCA, SVD Dimensionality Reduction
 
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#ScikitLearn #DimentionalityReduction #PCA #SVD #MachineLearning #DataAnalytics #DataScience Dimensionality reduction is an important step in data pre processing and data visualisation specially when we have large number of highly correlated features. In this tutorial, we apply Principal Component Analysis and Singular Value decomposition to boston housing and MNIST handwriting dataset and observe the effects of dimensionality reduction on accuracy. We also see how dimensionality reduction can be used to visualize data. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon
Views: 6854 The SemiColon
Text Analysis - Intro to Computer Science
 
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This video is part of an online course, Intro to Computer Science. Check out the course here: https://www.udacity.com/course/cs101.
Views: 1372 Udacity
Tariq Rashid - Dimension Reduction and Extracting Topics - A Gentle Introduction
 
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Filmed at PyData 2017 Description Text mining has many powerful methods for unlocking insights into the messy, ambiguous, but interesting text created by people. Singular value decomposition (SVD) is a useful method for reducing the many dimensions of text data, and distill out key themes in that text - called topic modelling or latent semantic analysis. This talk for beginners will gently explain SVD and how to use it. Abstract Text mining and natural language processing are hugely powerful fields that can unlock insights into the vast amounts of human knowledge, creativity and drivel (!) for automated computing. Examples include the fun of highlighting trends in internet chatter through to more serious analysis of finding patterns and links in leaked data sets of public interest. One key tool is to reduce the many dimensions of text data, and distill out the key themes in that text. People call this topic modelling, latent semantic analysis, and a few other names too. The powerful method at the heart of this is called singular value decomposition (SVD). This talk will gently introduce singular valued decomposition (SVD), explaining the mathematics in an accessible manner, and demonstrate how it can be used, using the Chilcot Iraq Report as an example dataset. Example code, notebooks and data sets are public on GitHub, and there is a blog for more discussion of this, and other text mining ideas http://makeyourowntextminingtoolkit.blogspot.co.uk www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. We aim to be an accessible, community-driven conference, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1040 PyData
Feature Selection Using R
 
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Provides steps for carrying out feature selection for building machine learning models using Boruta package. R code: https://goo.gl/h46Rv2 More ML videos: https://goo.gl/WHHqWP Feature selection is an important tool related to analyzing big data or working in data science field. 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: 1212 Bharatendra Rai
Parsing Text with R
 
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Using R to parse Text. Resources: **BOOK** "R for data science": http://amzn.to/2DjBqCg
Views: 1055 Tomer Ben David
Natural Language Processing With Python and NLTK p.1 Tokenizing words and Sentences
 
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Natural Language Processing is the task we give computers to read and understand (process) written text (natural language). By far, the most popular toolkit or API to do natural language processing is the Natural Language Toolkit for the Python programming language. The NLTK module comes packed full of everything from trained algorithms to identify parts of speech to unsupervised machine learning algorithms to help you train your own machine to understand a specific bit of text. NLTK also comes with a large corpora of data sets containing things like chat logs, movie reviews, journals, and much more! Bottom line, if you're going to be doing natural language processing, you should definitely look into NLTK! Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 366517 sentdex
Document Classification using Latent semantic analysis (LSA) in python | Sudharsan
 
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Document Classification using Latent semantic analysis (LSA) in python. You can also reach out to me on twitter: https://twitter.com/sudharsan1396 Code for this video: https://github.com/sudharsan13296/Document-Classification-using-LSA
Lecture 50 — Contextual Text Mining  Contextual Probabilistic Latent Semantic Analysis | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
R 1.1 - Initial Setup and Navigation
 
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Strategies that will make beginning R users more efficient: writing code using a "script" and navigating through directories within R.
Views: 136529 Google Developers
How to analyze text in Python
 
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In this video we look at how to start analysing the text from a file you import in to Python. For more info check out: https://www.udemy.com/python3-a-beginners-quick-start-guide-to-python/?couponCode=YOUTUBE_PROMO
Views: 1458 Tony Staunton