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Text Mining and Natural Language Processing in R

By: Minerva Singh

  • 06:08:28
  • 59
  • 6
  • Language: English

Course Summary

   Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

                    Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Read More

Target Audience

  1. People who wish to learn practical text mining and natural language processing
  2. People with prior experience of using RStudio
  3. People with prior experience of implementing machine learning techniques in R
  4. People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
  5. People who wish to derive insights from textual and social media data

Pre-Requisites

  1. Should have prior experience of R and RStudio
  2. Prior experience of statistical and machine learning techniques will be beneficial
  3. Should have an interest in learning practical text mining and natural language processing (NLP)
  4. Should have an interest in deriving insights from social media and text data

Curriculum

  • About the Course and Instructor
    07:59
  • Introduction to R and RStudio
    06:36
  • Conclusions to Section 1
    01:19
  • Read in CSV and Excel Data
    09:56
  • Read in Online CSV Data
    04:05
  • Read in Data from Zipped Folder
    03:04
  • Read in PDF Text Data
    08:33
  • Read in PDF Table Data
    04:39
  • Conclusions to Section 2
    01:04
  • Read in Data From Online Googlesheets
    04:04
  • Read in Data from Online HTML Tables-Part 1
    04:14
  • Read in Data from Online HTML Tables-Part 2
    06:24
  • Get and Clean Data from HTML Tables
    07:31
  • Read Text Data from an HTML Page
    08:52
  • Introduction to Selector Gadget
    06:11
  • More Webscraping With rvest-IMDB Webpage
    08:52
  • Another Way of Accessing Webpage Elements
    02:53
  • Conclusions to Section 3
    01:35
  • What is an API?
    02:34
  • Extract Text Data from Guardian Newspaper
    06:43
  • Extract Data from Facebook
    04:13
  • Get More out Of Facebook
    06:52
  • Set up a Twitter App for Mining Data from Twitter
    03:52
  • Extract Tweets Using R
    05:21
  • More Twitter Data Extraction Using R
    06:29
  • Get Tweet Locations
    05:07
  • Get Location Specific Trends
    02:02
  • Learn More About the Followers of a Twitter Handle
    06:55
  • Another Way of Extracting Information From Twitter- the rtweet Package
    03:19
  • Geolocation Specific Tweets With "rtweet"
    07:49
  • Locations of Tweets
    04:02
  • Mining Github Using R
    07:05
  • Set up the FourSquare App
    04:33
  • Extract Reviews for Venues on FourSquare
    11:28
  • Conclusions to Section 5
    01:47
  • Explore Tweet Data
    07:51
  • A Brief Explanation
    04:22
  • EDA With Text Data
    09:02
  • Examine Multiple Document Corpus of Text
    05:31
  • Brief Introduction to tidytext
    08:28
  • Text Exploration & Visualization with tidytext
    11:09
  • Explore Multiple Texts with tidytext
    09:22
  • Count Unique Words in Tweets
    04:54
  • Visualizing Text Data as TF-IDF
    07:55
  • TF-IDF in Graphical Form
    05:49
  • Conclusions to Section 6
    01:19
  • Wordclouds for Visualizing Tweet Sentiments: India's Demonetization Policy
    12:30
  • Wordclouds for Visualizing Reviews
    10:32
  • Tidy Wordclouds
    05:36
  • Quanteda Wordcloud
    08:34
  • Word Frequency in Text Data
    03:25
  • Tweet Sentiments- Mugabe's Ouster
    04:53
  • Tidy Sentiments- Sentiment Analysis Using tidytext
    08:38
  • Examine the Polarity of Text
    10:59
  • Examine the Polarity of Tweets
    06:24
  • Topic Modelling a Document
    08:15
  • Topic Modelling Multiple Documents
    14:19
  • Topic Modelling Tweets Using Quanteda
    08:22
  • Conclusion to Section 7
    02:15

About the Author

Minerva Singh, Data Scientist

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS.

Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online).

In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

More From Author

Text Mining and Natural Language Processing in R

  • 06:08:28
  • 59
  • 6
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

   Do You Want to Gain an Edge by Gleaning Novel Insights from Social Media?

                    Do You Want to Harness the Power of Unstructured Text and Social Media to Predict Trends?

Read More

Target Audience

  1. People who wish to learn practical text mining and natural language processing
  2. People with prior experience of using RStudio
  3. People with prior experience of implementing machine learning techniques in R
  4. People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
  5. People who wish to derive insights from textual and social media data

Pre-Requisites

  1. People who wish to learn practical text mining and natural language processing
  2. People with prior experience of using RStudio
  3. People with prior experience of implementing machine learning techniques in R
  4. People who were previously enrolled for my Data Science:Data Mining and Natural Language Processing course
  5. People who wish to derive insights from textual and social media data

About the Author

Minerva Singh, Data Scientist

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS.

Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online).

In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

More From Author