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