(Para) Pathology Notes
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Text Editing

Text Editing

Text Editing

  • Replace with a Subscript

Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Statistics for Social Data

Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Statistics for Social Data

Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Statistics for Social Data

Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Text Mining Courses

Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

Text Mining Journal Articles

  • E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses
  • Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.
  • Text mining of cancer-related information: Review of current status and future directions
  • Classification of Cancer-related Death Certificates using Machine Learning
  • E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses
  • Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.
  • Text mining of cancer-related information: Review of current status and future directions
  • Classification of Cancer-related Death Certificates using Machine Learning
  • E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses
  • Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.
  • Text mining of cancer-related information: Review of current status and future directions
  • Classification of Cancer-related Death Certificates using Machine Learning
  • E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses
  • Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.
  • Text mining of cancer-related information: Review of current status and future directions
  • Classification of Cancer-related Death Certificates using Machine Learning

Text Mining Journal Articles

  • E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses
  • Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.
  • Text mining of cancer-related information: Review of current status and future directions
  • Classification of Cancer-related Death Certificates using Machine Learning
  • Text Mining General
  • Text Mining General

Text Mining Orange

Text Mining Orange

Data sets for author name disambiguation: an empirical analysis and a new resource

A theoretical model of the relationship between the h-index and other simple citation indicators

Data sets for author name disambiguation: an empirical analysis and a new resource

A theoretical model of the relationship between the h-index and other simple citation indicators

Text Mining PubMed

Text Mining PubMed

Data sets for author name disambiguation: an empirical analysis and a new resource

A theoretical model of the relationship between the h-index and other simple citation indicators

Data sets for author name disambiguation: an empirical analysis and a new resource

A theoretical model of the relationship between the h-index and other simple citation indicators

Text Mining PubMed

Data sets for author name disambiguation: an empirical analysis and a new resource

A theoretical model of the relationship between the h-index and other simple citation indicators

Text Mining R

  • Analyzing Google Trends Data in R
  • Analyzing Google Trends Data in R
  • Analyzing Google Trends Data in R
  • Analyzing Google Trends Data in R

Text Mining R

  • Analyzing Google Trends Data in R

Text Mining Related Web Sites

  • import.io
  • parsehub
  • Regular Expression 101 is a very nice tool to identify regex codes for text mining
  • RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).
  • ExtendsClass is an online tool to visualize & test Regular Expressions
  • Downloadable statistical models for spaCy to predict and assign linguistic features
  • Industrial-Strength Natural Language Processing
  • import.io
  • parsehub
  • Regular Expression 101 is a very nice tool to identify regex codes for text mining
  • RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).
  • Downloadable statistical models for spaCy to predict and assign linguistic features
  • Industrial-Strength Natural Language Processing
  • import.io
  • parsehub
  • Regular Expression 101 is a very nice tool to identify regex codes for text mining
  • RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).
  • Downloadable statistical models for spaCy to predict and assign linguistic features
  • Industrial-Strength Natural Language Processing
  • import.io
  • parsehub
  • Regular Expression 101 is a very nice tool to identify regex codes for text mining
  • RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).
  • Downloadable statistical models for spaCy to predict and assign linguistic features
  • Industrial-Strength Natural Language Processing

Text Mining Related Web Sites

  • import.io
  • parsehub
  • Regular Expression 101 is a very nice tool to identify regex codes for text mining
  • RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).
  • Downloadable statistical models for spaCy to predict and assign linguistic features
  • Industrial-Strength Natural Language Processing

Text Mining Turkish

  • The Lucene stopwords.txt source code

​trstop​

  • The Lucene stopwords.txt source code

​trstop​

  • The Lucene stopwords.txt source code

​trstop​

  • The Lucene stopwords.txt source code

​trstop​

Text Mining Turkish

  • The Lucene stopwords.txt source code

​trstop​

Text Mining Twitter

  • Quick guide to mining twitter with R
  • Symplur
  • Symplur Signals for Research
  • Quick guide to mining twitter with R
  • Symplur
  • Symplur Signals for Research
  • Quick guide to mining twitter with R
  • Symplur
  • Symplur Signals for Research
  • Quick guide to mining twitter with R
  • Symplur
  • Symplur Signals for Research

Text Mining Twitter

  • Quick guide to mining twitter with R
  • Symplur
  • Symplur Signals for Research

Text Mining Videos

  • Text Mining (part 1) - Import Text into R (single document)
  • Text Mining (part 2) - Cleaning Text Data in R (single document)
  • Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)
  • Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R
  • Text Mining (part 5) - Import a Corpus in R
  • Text Mining (part 6) - Cleaning Corpus text in R
--
  • ​
    • N-gram word clouds in R ! Learn it in 5 minutes !
  • Word Cloud in R - Learn it in 4 minutes !
if you get error try this:
corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))
--
  • ​
    • Text Mining (part 1) - Import Text into R (single document)
  • Text Mining (part 2) - Cleaning Text Data in R (single document)
  • Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)
  • Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R
  • Text Mining (part 5) - Import a Corpus in R
  • Text Mining (part 6) - Cleaning Corpus text in R
--
  • ​
    • N-gram word clouds in R ! Learn it in 5 minutes !
  • Word Cloud in R - Learn it in 4 minutes !
if you get error try this:
corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))
--
  • ​
    • Text Mining (part 1) - Import Text into R (single document)
  • Text Mining (part 2) - Cleaning Text Data in R (single document)
  • Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)
  • Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R
  • Text Mining (part 5) - Import a Corpus in R
  • Text Mining (part 6) - Cleaning Corpus text in R
--
  • ​
    • N-gram word clouds in R ! Learn it in 5 minutes !
  • Word Cloud in R - Learn it in 4 minutes !
if you get error try this:
corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))
--
  • ​
    • Text Mining (part 1) - Import Text into R (single document)
  • Text Mining (part 2) - Cleaning Text Data in R (single document)
  • Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)
  • Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R
  • Text Mining (part 5) - Import a Corpus in R
  • Text Mining (part 6) - Cleaning Corpus text in R
--
  • ​
    • N-gram word clouds in R ! Learn it in 5 minutes !
  • Word Cloud in R - Learn it in 4 minutes !
if you get error try this:
corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))
--
  • Text Mining Videos
  • Text Mining (part 1) - Import Text into R (single document)
  • Text Mining (part 2) - Cleaning Text Data in R (single document)
  • Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)
  • Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R
  • Text Mining (part 5) - Import a Corpus in R
  • Text Mining (part 6) - Cleaning Corpus text in R
--
  • ​
    • N-gram word clouds in R ! Learn it in 5 minutes !
  • Word Cloud in R - Learn it in 4 minutes !
if you get error try this:
corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))
--
  • ​
    • Text Mining General
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