# Text Editing

## Text Editing

### Text Editing

* Replace with a Subscript

<http://www.brainbell.com/tutorials/ms-office/Word/Replace_With_A_Subscript.htm>

* <http://www.hemingwayapp.com/>
* <https://app.grammarly.com/>

## Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

<https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html>

## Statistics for Social Data

<http://ptrckprry.com/course/ssd/>

## Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

<https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html>

## Statistics for Social Data

<http://ptrckprry.com/course/ssd/>

## Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

<https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html>

## Statistics for Social Data

<http://ptrckprry.com/course/ssd/>

## Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

<https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html>

## Text Mining Courses

<http://ptrckprry.com/course/ssd/>

## Opinion Mining, Sentiment Analysis, and Opinion Spam Detection

<https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html>

## Text Mining Journal Articles

* E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068898/>

* Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

<https://www.ncbi.nlm.nih.gov/pubmed/28269893>

* Text mining of cancer-related information: Review of current status and future directions

<http://www.sciencedirect.com/science/article/pii/S1386505614001105>

* Classification of Cancer-related Death Certificates using Machine Learning

<https://www.researchgate.net/publication/237071357_Classification_of_Cancer-related_Death_Certificates_using_Machine_Learning>

* E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068898/>

* Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

<https://www.ncbi.nlm.nih.gov/pubmed/28269893>

* Text mining of cancer-related information: Review of current status and future directions

<http://www.sciencedirect.com/science/article/pii/S1386505614001105>

* Classification of Cancer-related Death Certificates using Machine Learning

<https://www.researchgate.net/publication/237071357_Classification_of_Cancer-related_Death_Certificates_using_Machine_Learning>

* E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068898/>

* Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

<https://www.ncbi.nlm.nih.gov/pubmed/28269893>

* Text mining of cancer-related information: Review of current status and future directions

<http://www.sciencedirect.com/science/article/pii/S1386505614001105>

* Classification of Cancer-related Death Certificates using Machine Learning

<https://www.researchgate.net/publication/237071357_Classification_of_Cancer-related_Death_Certificates_using_Machine_Learning>

* E-mail Address Harvesting on PubMed—A Call for Responsible Handling of E-mail Addresses

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068898/>

* Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

<https://www.ncbi.nlm.nih.gov/pubmed/28269893>

* Text mining of cancer-related information: Review of current status and future directions

<http://www.sciencedirect.com/science/article/pii/S1386505614001105>

* Classification of Cancer-related Death Certificates using Machine Learning

<https://www.researchgate.net/publication/237071357_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

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068898/>

* Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository.

<https://www.ncbi.nlm.nih.gov/pubmed/28269893>

* Text mining of cancer-related information: Review of current status and future directions

<http://www.sciencedirect.com/science/article/pii/S1386505614001105>

* Classification of Cancer-related Death Certificates using Machine Learning

<https://www.researchgate.net/publication/237071357_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

<https://link.springer.com/article/10.1007/s11192-017-2363-5?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2351-9?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2363-5?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2351-9?wt_mc=alerts.TOCjournals>

## Text Mining PubMed

## Text Mining PubMed

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

<https://link.springer.com/article/10.1007/s11192-017-2363-5?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2351-9?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2363-5?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2351-9?wt_mc=alerts.TOCjournals>

## Text Mining PubMed

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

<https://link.springer.com/article/10.1007/s11192-017-2363-5?wt_mc=alerts.TOCjournals>

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

<https://link.springer.com/article/10.1007/s11192-017-2351-9?wt_mc=alerts.TOCjournals>

## Text Mining R

* Analyzing Google Trends Data in R

<https://www.displayr.com/extracting-google-trends-data-in-r/?utm_source=Facebook&utm_medium=R> Programming\&utm\_campaign=google trends

* Analyzing Google Trends Data in R

<https://www.displayr.com/extracting-google-trends-data-in-r/?utm_source=Facebook&utm_medium=R> Programming\&utm\_campaign=google trends

* Analyzing Google Trends Data in R

<https://www.displayr.com/extracting-google-trends-data-in-r/?utm_source=Facebook&utm_medium=R> Programming\&utm\_campaign=google trends

* Analyzing Google Trends Data in R

<https://www.displayr.com/extracting-google-trends-data-in-r/?utm_source=Facebook&utm_medium=R> Programming\&utm\_campaign=google trends

## Text Mining R

* Analyzing Google Trends Data in R

<https://www.displayr.com/extracting-google-trends-data-in-r/?utm_source=Facebook&utm_medium=R> Programming\&utm\_campaign=google trends

## Text Mining Related Web Sites

* **import.io**

<https://www.import.io>

* **parsehub**

<https://www.parsehub.com/>

* **Regular Expression 101 is a very nice tool to identify regex codes for text mining**

<https://twitter.com/regex101>

<https://regex101.com/>

* **RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).**

<https://regexr.com/>

* **ExtendsClass is an online tool to visualize & test Regular Expressions**

<https://extendsclass.com/regex-tester.html>

* **Downloadable statistical models for spaCy to predict and assign linguistic features**

<https://spacy.io/models/>

* **Industrial-Strength Natural Language Processing**

<https://spacy.io/>

* **import.io**

<https://www.import.io>

* **parsehub**

<https://www.parsehub.com/>

* **Regular Expression 101 is a very nice tool to identify regex codes for text mining**

<https://twitter.com/regex101>

<https://regex101.com/>

* **RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).**

<https://regexr.com/>

* **Downloadable statistical models for spaCy to predict and assign linguistic features**

<https://spacy.io/models/>

* **Industrial-Strength Natural Language Processing**

<https://spacy.io/>

* **import.io**

<https://www.import.io>

* **parsehub**

<https://www.parsehub.com/>

* **Regular Expression 101 is a very nice tool to identify regex codes for text mining**

<https://twitter.com/regex101>

<https://regex101.com/>

* **RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).**

<https://regexr.com/>

* **Downloadable statistical models for spaCy to predict and assign linguistic features**

<https://spacy.io/models/>

* **Industrial-Strength Natural Language Processing**

<https://spacy.io/>

* **import.io**

<https://www.import.io>

* **parsehub**

<https://www.parsehub.com/>

* **Regular Expression 101 is a very nice tool to identify regex codes for text mining**

<https://twitter.com/regex101>

<https://regex101.com/>

* **RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).**

<https://regexr.com/>

* **Downloadable statistical models for spaCy to predict and assign linguistic features**

<https://spacy.io/models/>

* **Industrial-Strength Natural Language Processing**

<https://spacy.io/>

## Text Mining Related Web Sites

* **import.io**

<https://www.import.io>

* **parsehub**

<https://www.parsehub.com/>

* **Regular Expression 101 is a very nice tool to identify regex codes for text mining**

<https://twitter.com/regex101>

<https://regex101.com/>

* **RegExr is an online tool to learn, build & test Regular Expressions (RegEx / RegExp).**

<https://regexr.com/>

* **Downloadable statistical models for spaCy to predict and assign linguistic features**

<https://spacy.io/models/>

* **Industrial-Strength Natural Language Processing**

<https://spacy.io/>

## Text Mining Turkish

* The Lucene *stopwords.txt* source code

<https://alvinalexander.com/java/jwarehouse/lucene/contrib/analyzers/common/src/resources/org/apache/lucene/analysis/tr/stopwords.txt.shtml>

* [Türkçe Etkisiz Kelimeler (Stop Words) Listesi 1.1](http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35)

<http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35>

* [**TextRank**](https://github.com/crodas/TextRank)

<https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt>

* [**stopwords-tr**](https://github.com/stopwords-iso/stopwords-tr)

<https://github.com/stopwords-iso/stopwords-tr>

* [**Turkish-Stopwords**](https://github.com/tkorkunckaya/Turkish-Stopwords)

<https://github.com/tkorkunckaya/Turkish-Stopwords>

## [**trstop**](https://github.com/ahmetax/trstop)

## <https://github.com/ahmetax/trstop>

* The Lucene *stopwords.txt* source code

<https://alvinalexander.com/java/jwarehouse/lucene/contrib/analyzers/common/src/resources/org/apache/lucene/analysis/tr/stopwords.txt.shtml>

* [Türkçe Etkisiz Kelimeler (Stop Words) Listesi 1.1](http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35)

<http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35>

* [**TextRank**](https://github.com/crodas/TextRank)

<https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt>

* [**stopwords-tr**](https://github.com/stopwords-iso/stopwords-tr)

<https://github.com/stopwords-iso/stopwords-tr>

* [**Turkish-Stopwords**](https://github.com/tkorkunckaya/Turkish-Stopwords)

<https://github.com/tkorkunckaya/Turkish-Stopwords>

## [**trstop**](https://github.com/ahmetax/trstop)

## <https://github.com/ahmetax/trstop>

* The Lucene *stopwords.txt* source code

<https://alvinalexander.com/java/jwarehouse/lucene/contrib/analyzers/common/src/resources/org/apache/lucene/analysis/tr/stopwords.txt.shtml>

* [Türkçe Etkisiz Kelimeler (Stop Words) Listesi 1.1](http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35)

<http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35>

* [**TextRank**](https://github.com/crodas/TextRank)

<https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt>

* [**stopwords-tr**](https://github.com/stopwords-iso/stopwords-tr)

<https://github.com/stopwords-iso/stopwords-tr>

* [**Turkish-Stopwords**](https://github.com/tkorkunckaya/Turkish-Stopwords)

<https://github.com/tkorkunckaya/Turkish-Stopwords>

## [**trstop**](https://github.com/ahmetax/trstop)

## <https://github.com/ahmetax/trstop>

* The Lucene *stopwords.txt* source code

<https://alvinalexander.com/java/jwarehouse/lucene/contrib/analyzers/common/src/resources/org/apache/lucene/analysis/tr/stopwords.txt.shtml>

* [Türkçe Etkisiz Kelimeler (Stop Words) Listesi 1.1](http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35)

<http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35>

* [**TextRank**](https://github.com/crodas/TextRank)

<https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt>

* [**stopwords-tr**](https://github.com/stopwords-iso/stopwords-tr)

<https://github.com/stopwords-iso/stopwords-tr>

* [**Turkish-Stopwords**](https://github.com/tkorkunckaya/Turkish-Stopwords)

<https://github.com/tkorkunckaya/Turkish-Stopwords>

## [**trstop**](https://github.com/ahmetax/trstop)

## <https://github.com/ahmetax/trstop>

## Text Mining Turkish

* The Lucene *stopwords.txt* source code

<https://alvinalexander.com/java/jwarehouse/lucene/contrib/analyzers/common/src/resources/org/apache/lucene/analysis/tr/stopwords.txt.shtml>

* [Türkçe Etkisiz Kelimeler (Stop Words) Listesi 1.1](http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35)

<http://www.turkceogretimi.com/Genel-Konular/article/541-turkce-etkisiz-kelimeler-stop-words-listesi-11/35>

* [**TextRank**](https://github.com/crodas/TextRank)

<https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt>

* [**stopwords-tr**](https://github.com/stopwords-iso/stopwords-tr)

<https://github.com/stopwords-iso/stopwords-tr>

* [**Turkish-Stopwords**](https://github.com/tkorkunckaya/Turkish-Stopwords)

<https://github.com/tkorkunckaya/Turkish-Stopwords>

## [**trstop**](https://github.com/ahmetax/trstop)

## <https://github.com/ahmetax/trstop>

## Text Mining Twitter

* Quick guide to mining twitter with R

<https://sites.google.com/site/miningtwitter/home>

* Symplur

<https://www.symplur.com/healthcare-hashtags/pathology/>

<https://www.symplur.com/blog/introducing-pathology-hashtag-ontology/>

* Symplur Signals for Research

<https://www.youtube.com/watch?v=7mmQCFjpDtk&list=WL&index=15>

* Quick guide to mining twitter with R

<https://sites.google.com/site/miningtwitter/home>

* Symplur

<https://www.symplur.com/healthcare-hashtags/pathology/>

<https://www.symplur.com/blog/introducing-pathology-hashtag-ontology/>

* Symplur Signals for Research

<https://www.youtube.com/watch?v=7mmQCFjpDtk&list=WL&index=15>

* Quick guide to mining twitter with R

<https://sites.google.com/site/miningtwitter/home>

* Symplur

<https://www.symplur.com/healthcare-hashtags/pathology/>

<https://www.symplur.com/blog/introducing-pathology-hashtag-ontology/>

* Symplur Signals for Research

<https://www.youtube.com/watch?v=7mmQCFjpDtk&list=WL&index=15>

* Quick guide to mining twitter with R

<https://sites.google.com/site/miningtwitter/home>

* Symplur

<https://www.symplur.com/healthcare-hashtags/pathology/>

<https://www.symplur.com/blog/introducing-pathology-hashtag-ontology/>

* Symplur Signals for Research

<https://www.youtube.com/watch?v=7mmQCFjpDtk&list=WL&index=15>

## Text Mining Twitter

* Quick guide to mining twitter with R

<https://sites.google.com/site/miningtwitter/home>

* Symplur

<https://www.symplur.com/healthcare-hashtags/pathology/>

<https://www.symplur.com/blog/introducing-pathology-hashtag-ontology/>

* Symplur Signals for Research

<https://www.youtube.com/watch?v=7mmQCFjpDtk&list=WL&index=15>

## Text Mining Videos

* Text Mining (part 1) - Import Text into R (single document)

<https://www.youtube.com/watch?v=fga5gLtFQs0&index=2&list=WL>

* Text Mining (part 2) - Cleaning Text Data in R (single document)

<https://www.youtube.com/watch?v=gtQWMxWzs_M&list=WL&index=2>

* Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)

<https://www.youtube.com/watch?v=JM_J7ufS-BU&t=0s>

* Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R

<https://www.youtube.com/watch?v=WfoVINuxIJA&index=11&list=WL>

<http://ptrckprry.com/course/ssd/data/positive-words.txt>

<http://ptrckprry.com/course/ssd/data/negative-words.txt>

* Text Mining (part 5) - Import a Corpus in R

<https://www.youtube.com/watch?v=pFinlXYLZ-A&list=WL&index=14>

* Text Mining (part 6) - Cleaning Corpus text in R

<https://www.youtube.com/watch?v=jCrQYOsAcv4&list=WL&index=24>

\--

* * N-gram word clouds in R ! Learn it in 5 minutes !

<https://www.youtube.com/watch?v=HellsQ2JF2k&feature=youtu.be>

* Word Cloud in R - Learn it in 4 minutes !

<https://www.youtube.com/watch?v=oVVvG035vQc>

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)

<https://www.youtube.com/watch?v=fga5gLtFQs0&index=2&list=WL>

* Text Mining (part 2) - Cleaning Text Data in R (single document)

<https://www.youtube.com/watch?v=gtQWMxWzs_M&list=WL&index=2>

* Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)

<https://www.youtube.com/watch?v=JM_J7ufS-BU&t=0s>

* Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R

<https://www.youtube.com/watch?v=WfoVINuxIJA&index=11&list=WL>

<http://ptrckprry.com/course/ssd/data/positive-words.txt>

<http://ptrckprry.com/course/ssd/data/negative-words.txt>

* Text Mining (part 5) - Import a Corpus in R

<https://www.youtube.com/watch?v=pFinlXYLZ-A&list=WL&index=14>

* Text Mining (part 6) - Cleaning Corpus text in R

<https://www.youtube.com/watch?v=jCrQYOsAcv4&list=WL&index=24>

\--

* * N-gram word clouds in R ! Learn it in 5 minutes !

<https://www.youtube.com/watch?v=HellsQ2JF2k&feature=youtu.be>

* Word Cloud in R - Learn it in 4 minutes !

<https://www.youtube.com/watch?v=oVVvG035vQc>

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)

<https://www.youtube.com/watch?v=fga5gLtFQs0&index=2&list=WL>

* Text Mining (part 2) - Cleaning Text Data in R (single document)

<https://www.youtube.com/watch?v=gtQWMxWzs_M&list=WL&index=2>

* Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)

<https://www.youtube.com/watch?v=JM_J7ufS-BU&t=0s>

* Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R

<https://www.youtube.com/watch?v=WfoVINuxIJA&index=11&list=WL>

<http://ptrckprry.com/course/ssd/data/positive-words.txt>

<http://ptrckprry.com/course/ssd/data/negative-words.txt>

* Text Mining (part 5) - Import a Corpus in R

<https://www.youtube.com/watch?v=pFinlXYLZ-A&list=WL&index=14>

* Text Mining (part 6) - Cleaning Corpus text in R

<https://www.youtube.com/watch?v=jCrQYOsAcv4&list=WL&index=24>

\--

* * N-gram word clouds in R ! Learn it in 5 minutes !

<https://www.youtube.com/watch?v=HellsQ2JF2k&feature=youtu.be>

* Word Cloud in R - Learn it in 4 minutes !

<https://www.youtube.com/watch?v=oVVvG035vQc>

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)

<https://www.youtube.com/watch?v=fga5gLtFQs0&index=2&list=WL>

* Text Mining (part 2) - Cleaning Text Data in R (single document)

<https://www.youtube.com/watch?v=gtQWMxWzs_M&list=WL&index=2>

* Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)

<https://www.youtube.com/watch?v=JM_J7ufS-BU&t=0s>

* Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R

<https://www.youtube.com/watch?v=WfoVINuxIJA&index=11&list=WL>

<http://ptrckprry.com/course/ssd/data/positive-words.txt>

<http://ptrckprry.com/course/ssd/data/negative-words.txt>

* Text Mining (part 5) - Import a Corpus in R

<https://www.youtube.com/watch?v=pFinlXYLZ-A&list=WL&index=14>

* Text Mining (part 6) - Cleaning Corpus text in R

<https://www.youtube.com/watch?v=jCrQYOsAcv4&list=WL&index=24>

\--

* * N-gram word clouds in R ! Learn it in 5 minutes !

<https://www.youtube.com/watch?v=HellsQ2JF2k&feature=youtu.be>

* Word Cloud in R - Learn it in 4 minutes !

<https://www.youtube.com/watch?v=oVVvG035vQc>

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)

<https://www.youtube.com/watch?v=fga5gLtFQs0&index=2&list=WL>

* Text Mining (part 2) - Cleaning Text Data in R (single document)

<https://www.youtube.com/watch?v=gtQWMxWzs_M&list=WL&index=2>

* Text Mining (part 3) - Sentiment Analysis and Wordcloud in R (single document)

<https://www.youtube.com/watch?v=JM_J7ufS-BU&t=0s>

* Text Mining (part4) - Postive and Negative Terms for Sentiment Analysis in R

<https://www.youtube.com/watch?v=WfoVINuxIJA&index=11&list=WL>

<http://ptrckprry.com/course/ssd/data/positive-words.txt>

<http://ptrckprry.com/course/ssd/data/negative-words.txt>

* Text Mining (part 5) - Import a Corpus in R

<https://www.youtube.com/watch?v=pFinlXYLZ-A&list=WL&index=14>

* Text Mining (part 6) - Cleaning Corpus text in R

<https://www.youtube.com/watch?v=jCrQYOsAcv4&list=WL&index=24>

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* * N-gram word clouds in R ! Learn it in 5 minutes !

<https://www.youtube.com/watch?v=HellsQ2JF2k&feature=youtu.be>

* Word Cloud in R - Learn it in 4 minutes !

<https://www.youtube.com/watch?v=oVVvG035vQc>

if you get error try this:

`corpus <- tm_map(corpus,content_transformer(function(x) iconv(x, "latin1", "ASCII", sub="")))`

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* * Text Mining General