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

• TextRank

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

• stopwords-tr

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

• Turkish-Stopwords

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

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

• TextRank

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

• stopwords-tr

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

• Turkish-Stopwords

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

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

• TextRank

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

• stopwords-tr

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

• Turkish-Stopwords

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

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

• TextRank

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

• stopwords-tr

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

• Turkish-Stopwords

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

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

• TextRank

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

• stopwords-tr

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

• Turkish-Stopwords

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

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

--

• • 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 General