Text Editing
Text Editing
Text Editing
Replace with a Subscript
http://www.brainbell.com/tutorials/ms-office/Word/Replace_With_A_Subscript.htm
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
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
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
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
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
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
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
https://extendsclass.com/regex-tester.html
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
https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt
https://github.com/stopwords-iso/stopwords-tr
https://github.com/tkorkunckaya/Turkish-Stopwords
The Lucene stopwords.txt source code
https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt
https://github.com/stopwords-iso/stopwords-tr
https://github.com/tkorkunckaya/Turkish-Stopwords
The Lucene stopwords.txt source code
https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt
https://github.com/stopwords-iso/stopwords-tr
https://github.com/tkorkunckaya/Turkish-Stopwords
The Lucene stopwords.txt source code
https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt
https://github.com/stopwords-iso/stopwords-tr
https://github.com/tkorkunckaya/Turkish-Stopwords
Text Mining Turkish
The Lucene stopwords.txt source code
https://github.com/crodas/TextRank/blob/master/lib/TextRank/Stopword/turkish-stopwords.txt
https://github.com/stopwords-iso/stopwords-tr
https://github.com/tkorkunckaya/Turkish-Stopwords
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
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