Package 'qdapDictionaries'

Title: Dictionaries and Word Lists for the 'qdap' Package
Description: A collection of text analysis dictionaries and word lists for use with the 'qdap' package.
Authors: Tyler Rinker
Maintainer: Tyler Rinker <[email protected]>
License: GPL-2
Version: 1.0.8
Built: 2024-03-31 05:10:11 UTC
Source: https://github.com/trinker/qdapdictionaries

Help Index


Small Abbreviations Data Set

Description

A dataset containing abbreviations and their qdap friendly form.

Usage

data(abbreviations)

Format

A data frame with 14 rows and 2 variables

Details


Action Word List

Description

A dataset containing a vector of action words. This is a subset of the Moby project: Moby Part-of-Speech.

Usage

data(action.verbs)

Format

A vector with 1569 elements

Details

From Grady Ward's Moby project: "This second edition is a particularly thorough revision of the original Moby Part-of-Speech. Beyond the fifteen thousand new entries, many thousand more entries have been scrutinized for correctness and modernity. This is unquestionably the largest P-O-S list in the world. Note that the many included phrases means that parsing algorithms can now tokenize in units larger than a single word, increasing both speed and accuracy."


Adverb Word List

Description

A dataset containing a vector of adverbs words. This is a subset of the Moby project: Moby Part-of-Speech.

Usage

data(adverb)

Format

A vector with 13398 elements

Details

From Grady Ward's Moby project: "This second edition is a particularly thorough revision of the original Moby Part-of-Speech. Beyond the fifteen thousand new entries, many thousand more entries have been scrutinized for correctness and modernity. This is unquestionably the largest P-O-S list in the world. Note that the many included phrases means that parsing algorithms can now tokenize in units larger than a single word, increasing both speed and accuracy."


Amplifying Words

Description

A dataset containing a vector of words that amplify word meaning.

Usage

data(amplification.words)

Format

A vector with 49 elements

Details

Valence shifters are words that alter or intensify the meaning of the polarized words and include negators and amplifiers. Negators are, generally, adverbs that negate sentence meaning; for example the word like in the sentence, "I do like pie.", is given the opposite meaning in the sentence, "I do not like pie.", now containing the negator not. Amplifiers are, generally, adverbs or adjectives that intensify sentence meaning. Using our previous example, the sentiment of the negator altered sentence, "I seriously do not like pie.", is heightened with addition of the amplifier seriously. Whereas de-amplifiers decrease the intensity of a polarized word as in the sentence "I barely like pie"; the word "barely" deamplifies the word like.


Buckley & Salton Stopword List

Description

A stopword list containing a character vector of stopwords.

Usage

data(BuckleySaltonSWL)

Format

A character vector with 546 elements

Details

From Onix Text Retrieval Toolkit API Reference: "This stopword list was built by Gerard Salton and Chris Buckley for the experimental SMART information retrieval system at Cornell University. This stopword list is generally considered to be on the larger side and so when it is used, some implementations edit it so that it is better suited for a given domain and audience while others use this stopword list as it stands."

Note

Reduced from the original 571 words to 546.

References

http://www.lextek.com/manuals/onix/stopwords2.html


Contraction Conversions

Description

A dataset containing common contractions and their expanded form.

Usage

data(contractions)

Format

A data frame with 70 rows and 2 variables

Details


De-amplifying Words

Description

A dataset containing a vector of words that de-amplify word meaning.

Usage

data(deamplification.words)

Format

A vector with 13 elements

Details

Valence shifters are words that alter or intensify the meaning of the polarized words and include negators and amplifiers. Negators are, generally, adverbs that negate sentence meaning; for example the word like in the sentence, "I do like pie.", is given the opposite meaning in the sentence, "I do not like pie.", now containing the negator not. Amplifiers are, generally, adverbs or adjectives that intensify sentence meaning. Using our previous example, the sentiment of the negator altered sentence, "I seriously do not like pie.", is heightened with addition of the amplifier seriously. Whereas de-amplifiers decrease the intensity of a polarized word as in the sentence "I barely like pie"; the word "barely" deamplifies the word like.


Nettalk Corpus Syllable Data Set

Description

A dataset containing syllable counts.

Usage

data(DICTIONARY)

Format

A data frame with 20137 rows and 2 variables

Details

Note

This data set is based on the Nettalk Corpus but has some researcher word deletions and additions based on the needs of the syllable_sum algorithm.

References

Sejnowski, T.J., and Rosenberg, C.R. (1987). "Parallel networks that learn to pronounce English text" in Complex Systems, 1, 145-168. Retrieved from: http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Nettalk+Corpus)

UCI Machine Learning Repository website


Alemany's Discourse Markers

Description

A dataset containing discourse markers

Usage

data(discourse.markers.alemany)

Format

A data frame with 97 rows and 5 variables

Details

A dictionary of discourse markers from Alemany (2005). "In this lexicon, discourse markers are characterized by their structural (continuation or elaboration) and semantic (revision, cause, equality, context) meanings, and they are also associated to a morphosyntactic class (part of speech, PoS), one of adverbial (A), phrasal (P) or conjunctive (C)... Sometimes a discourse marker is underspecified with respect to a meaning. We encode this with a hash. This tends to happen with structural meanings, because these meanings can well be established by discursive mechanisms other than discourse markers, and the presence of the discourse marker just reinforces the relation, whichever it may be." (p. 191).

References

Alemany, L. A. (2005). Representing discourse for automatic text summarization via shallow NLP techniques (Unpublished doctoral dissertation). Universitat de Barcelona, Barcelona.

http://www.cs.famaf.unc.edu.ar/~laura/shallowdisc4summ/tesi_electronica.pdf
http://russell.famaf.unc.edu.ar/~laura/shallowdisc4summ/discmar/#description


Dolch List of 220 Common Words

Description

Edward William Dolch's list of 220 Most Commonly Used Words.

Usage

data(Dolch)

Format

A vector with 220 elements

Details

Dolch's Word List made up 50-75% of all printed text in 1936.

References

Dolch, E. W. (1936). A basic sight vocabulary. Elementary School Journal, 36, 456-460.


Emoticons Data Set

Description

A dataset containing common emoticons (adapted from Popular Emoticon List).

Usage

data(emoticon)

Format

A data frame with 81 rows and 2 variables

Details

References

http://www.lingo2word.com/lists/emoticon_listH.html


Fry's 1000 Most Commonly Used English Words

Description

A stopword list containing a character vector of stopwords.

Usage

data(Fry_1000)

Format

A vector with 1000 elements

Details

Fry's 1000 Word List makes up 90% of all printed text.

References

Fry, E. B. (1997). Fry 1000 instant words. Lincolnwood, IL: Contemporary Books.


Function Words

Description

A vector of function words from John and Muriel Higgins's list used for the text game ECLIPSE. The lest is augmented with additional contractions from contractions.

Usage

data(function.words)

Format

A vector with 350 elements

References

http://myweb.tiscali.co.uk/wordscape/museum/funcword.html


Augmented List of Grady Ward's English Words and Mark Kantrowitz's Names List

Description

A dataset containing a vector of Grady Ward's English words augmented with DICTIONARY, Mark Kantrowitz's names list, other proper nouns, and contractions.

Usage

data(GradyAugmented)

Format

A vector with 122806 elements

Details

A dataset containing a vector of Grady Ward's English words augmented with proper nouns (U.S. States, Countries, Mark Kantrowitz's Names List, and months) and contractions. That dataset is augmented for spell checking purposes.

References

Moby Thesaurus List by Grady Ward http://www.gutenberg.org

List of names from Mark Kantrowitz http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/nlp/corpora/names/. A copy of the README is available here per the author's request.


Interjections

Description

A dataset containing a character vector of common interjections.

Usage

data(interjections)

Format

A character vector with 139 elements

References

http://www.vidarholen.net/contents/interjections/


Polarity Lookup Key

Description

A dataset containing a polarity lookup key (see polarity).

Usage

data(key.pol)

Format

A hash key with words and corresponding values.

References

Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.

http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html


Power Lookup Key

Description

A dataset containing a power lookup key.

Usage

data(key.power)

Format

A hash key with power words.

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt


Strength Lookup Key

Description

A dataset containing a strength lookup key.

Usage

data(key.strength)

Format

A hash key with strength words.

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt


Syllable Lookup Key

Description

A dataset containing a syllable lookup key (see DICTIONARY).

Usage

data(key.syl)

Format

A hash key with a modified DICTIONARY data set.

Details

For internal use.

References

UCI Machine Learning Repository website


Synonym Lookup Key

Description

A dataset containing a synonym lookup key.

Usage

data(key.syn)

Format

A hash key with 10976 rows and 2 variables (words and synonyms).

References

Scraped from: Reverso Online Dictionary. The word list fed to Reverso is the unique words from the combination of DICTIONARY and labMT.


Language Assessment by Mechanical Turk (labMT) Sentiment Words

Description

A dataset containing words, average happiness score (polarity), standard deviations, and rankings.

Usage

data(labMT)

Format

A data frame with 10222 rows and 8 variables

Details

References

Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., & Danforth, C.M. (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLoS ONE 6(12): e26752. doi:10.1371/journal.pone.0026752

http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0026752.s001


Leveled Dolch List of 220 Common Words

Description

Edward William Dolch's list of 220 Most Commonly Used Words by reading level.

Usage

data(Leveled_Dolch)

Format

A data frame with 220 rows and 2 variables

Details

Dolch's Word List made up 50-75% of all printed text in 1936.

References

Dolch, E. W. (1936). A basic sight vocabulary. Elementary School Journal, 36, 456-460.


First Names and Gender (U.S.)

Description

A dataset containing 1990 U.S. census data on first names.

Usage

data(NAMES)

Format

A data frame with 5493 rows and 7 variables

Details

References

http://www.census.gov


First Names and Predictive Gender (U.S.) List

Description

A list version of the NAMES_SEX dataset broken down by first letter.

Usage

data(NAMES_LIST)

Format

A list with 26 elements

Details

Alphabetical list of dataframes with the following variables:

References

http://www.census.gov


First Names and Predictive Gender (U.S.)

Description

A truncated version of the NAMES dataset used for predicting.

Usage

data(NAMES_SEX)

Format

A data frame with 5162 rows and 3 variables

Details

References

http://www.census.gov


Negating Words

Description

A dataset containing a vector of words that negate word meaning.

Usage

data(negation.words)

Format

A vector with 23 elements

Details

Valence shifters are words that alter or intensify the meaning of the polarized words and include negators and amplifiers. Negators are, generally, adverbs that negate sentence meaning; for example the word like in the sentence, "I do like pie.", is given the opposite meaning in the sentence, "I do not like pie.", now containing the negator not. Amplifiers are, generally, adverbs or adjectives that intensify sentence meaning. Using our previous example, the sentiment of the negator altered sentence, "I seriously do not like pie.", is heightened with addition of the amplifier seriously. Whereas de-amplifiers decrease the intensity of a polarized word as in the sentence "I barely like pie"; the word "barely" deamplifies the word like.


Negative Words

Description

A dataset containing a vector of negative words.

Usage

data(negative.words)

Format

A vector with 4776 elements

Details

A sentence containing more negative words would be deemed a negative sentence, whereas a sentence containing more positive words would be considered positive.

References

Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.

http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html


Onix Text Retrieval Toolkit Stopword List 1

Description

A stopword list containing a character vector of stopwords.

Usage

data(OnixTxtRetToolkitSWL1)

Format

A character vector with 404 elements

Details

From Onix Text Retrieval Toolkit API Reference: "This stopword list is probably the most widely used stopword list. It covers a wide number of stopwords without getting too aggressive and including too many words which a user might search upon."

Note

Reduced from the original 429 words to 404.

References

http://www.lextek.com/manuals/onix/stopwords1.html


Positive Words

Description

A dataset containing a vector of positive words.

Usage

data(positive.words)

Format

A vector with 2003 elements

Details

A sentence containing more negative words would be deemed a negative sentence, whereas a sentence containing more positive words would be considered positive.

References

Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.

http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html


Words that Indicate Power

Description

A subset of the Harvard IV Dictionary containing a vector of words indicating power.

Usage

data(power.words)

Format

A vector with 624 elements

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt


Preposition Words

Description

A dataset containing a vector of common prepositions.

Usage

data(preposition)

Format

A vector with 162 elements


Prints a view_data Object

Description

Prints a view_data object.

Usage

## S3 method for class 'view_data'
print(x, ...)

Arguments

x

The view_data object.

...

ignored


qdapDictionaries

Description

A collection of dictionaries and Word Lists to Accompany the qdap Package


Words that Indicate Strength

Description

A subset of the Harvard IV Dictionary containing a vector of words indicating strength.

Usage

data(strong.words)

Format

A vector with 1474 elements

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt


Words that Indicate Submission

Description

A subset of the Harvard IV Dictionary containing a vector of words indicating submission.

Usage

data(submit.words)

Format

A vector with 262 elements

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt


Fry's 100 Most Commonly Used English Words

Description

A stopword list containing a character vector of stopwords.

Usage

data(Top100Words)

Format

A character vector with 100 elements

Details

Fry's Word List: The first 25 make up about one-third of all printed material in English. The first 100 make up about one-half of all printed material in English. The first 300 make up about 65% of all printed material in English."

References

Fry, E. B. (1997). Fry 1000 instant words. Lincolnwood, IL: Contemporary Books.


Fry's 200 Most Commonly Used English Words

Description

A stopword list containing a character vector of stopwords.

Usage

data(Top200Words)

Format

A character vector with 200 elements

Details

Fry's Word List: The first 25 make up about one-third of all printed material in English. The first 100 make up about one-half of all printed material in English. The first 300 make up about 65% of all printed material in English."

References

Fry, E. B. (1997). Fry 1000 instant words. Lincolnwood, IL: Contemporary Books.


Fry's 25 Most Commonly Used English Words

Description

A stopword list containing a character vector of stopwords.

Usage

data(Top25Words)

Format

A character vector with 25 elements

Details

Fry's Word List: The first 25 make up about one-third of all printed material in English. The first 100 make up about one-half of all printed material in English. The first 300 make up about 65% of all printed material in English."

References

Fry, E. B. (1997). Fry 1000 instant words. Lincolnwood, IL: Contemporary Books.


List all data sets available in a qdapDictionaries

Description

Lists and describes all the data sets available in qdapDictionaries.

Usage

view_data(package = "qdapDictionaries")

Arguments

package

The name of the package.

Value

Returns the data sets of qdapDictionaries as a dataframe.

See Also

data

Examples

view_data()

Words that Indicate Weakness

Description

A subset of the Harvard IV Dictionary containing a vector of words indicating weakness.

Usage

data(weak.words)

Format

A vector with 647 elements

References

http://www.wjh.harvard.edu/~inquirer/inqdict.txt