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