Nothing
R/sentimentr-package.R
In sentimentr: Calculate Text Polarity Sentiment
#' Calculate Text Polarity Sentiment
#'
#' Calculate text polarity sentiment in the English language at the sentence
#' level and optionally aggregate by rows or grouping variable(s).
#' @docType package
#' @name sentimentr
#' @aliases sentimentr package-sentiment
NULL
#' Sam I Am Text
#'
#' A dataset containing a character vector of the text from Seuss's 'Sam I Am'.
#'
#' @docType data
#' @keywords datasets
#' @name sam_i_am
#' @usage data(sam_i_am)
#' @format A character vector with 169 elements
#' @references Seuss, Dr. (1960). Green Eggs and Ham.
NULL
#' 2012 U.S. Presidential Debates
#'
#' A dataset containing a cleaned version of all three presidential debates for
#' the 2012 election.
#'
#' @details
#' \itemize{
#' \item person. The speaker
#' \item tot. Turn of talk
#' \item dialogue. The words spoken
#' \item time. Variable indicating which of the three debates the dialogue is from
#' }
#'
#' @docType data
#' @keywords datasets
#' @name presidential_debates_2012
#' @usage data(presidential_debates_2012)
#' @format A data frame with 2912 rows and 4 variables
NULL
#' Movie Reviews
#'
#' A dataset containing sentiment scored movie reviews from a Kaggle competition
#' posted by University of Michigan SI650. The data was originally collected
#' from opinmind.com.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the review
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kaggle_movie_reviews
#' @usage data(kaggle_movie_reviews)
#' @format A data frame with 7,086 rows and 2 variables
#' @references Original URL: https://www.kaggle.com/c/si650winter11/data
NULL
#' Sentiment Scored New York Times Articles
#'
#' A dataset containing Hutto & Gilbert's (2014) sentiment scored New York Times
#' articles.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the article
#' }
#'
#' Vadar's Liscense:
#'
#' The MIT License (MIT)
#'
#' Copyright (c) 2016 C.J. Hutto
#'
#' Permission is hereby granted, free of charge, to any person obtaining a copy
#' of this software and associated documentation files (the "Software"), to deal
#' in the Software without restriction, including without limitation the rights
#' to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#' copies of the Software, and to permit persons to whom the Software is
#' furnished to do so, subject to the following conditions:
#'
#' The above copyright notice and this permission notice shall be included in all
#' copies or substantial portions of the Software.
#'
#' THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#' IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#' FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#' AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#' LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#' OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#' SOFTWARE.
#' @docType data
#' @keywords datasets
#' @name nyt_articles
#' @usage data(nyt_articles)
#' @format A data frame with 5,179 rows and 2 variables
#' @references
#' Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model
#' for Sentiment Analysis of Social Media Text. Eighth International Conference
#' on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
#'
#' Original URL: https://github.com/cjhutto/vaderSentiment
NULL
#' Student Course Evaluation Comments
#'
#' A dataset containing a subset of comments and rating from Welch & Mihalcea's
#' (2017) data set filtered to include comments with a one or more unambiguous
#' sentiment rating.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the evaluation
#' }
#'
#' @docType data
#' @keywords datasets
#' @name course_evaluations
#' @usage data(course_evaluations)
#' @format A data frame with 566 rows and 2 variables
#' @references Welch, C. and Mihalcea, R. (2017). Targeted sentiment to
#' understand student comments. In Proceedings of the International Conference
#' on Computational Linguistics (COLING 2016). \cr \cr
#' Original URL: http://web.eecs.umich.edu/~mihalcea/downloads.html#GroundedEmotions
NULL
#' Hotel Reviews
#'
#' A dataset containing a random sample (n = 5000 of 1,621,956) of Wang, Lu, &
#' Zhai's (2011) hotel reviews data set scraped by the authors from
#' Original URL: http://www.tripadvisor.com.
#'
#' @details
#' \itemize{
#' \item sentiment. The overall rating for the experience
#' \item text. The text review of the hotel
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hotel_reviews
#' @usage data(hotel_reviews)
#' @format A data frame with 5000 rows and 2 variables
#' @references Wang, H., Lu, Y., and Zhai, C. (2011). Latent aspect rating
#' analysis without aspect keyword supervision. In Proceedings of the 17th ACM
#' SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2011), 618-626. \cr \cr
#' Original URL: 'http://sifaka.cs.uiuc.edu/~wang296/Data/index.html'
NULL
#' Kotzias Reviews: Amazon Cells
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_amazon_cells
#' @usage data(kotzias_reviews_amazon_cells)
#' @format A data frame with 1,067 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Kotzias Reviews: IMBD
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_imdb
#' @usage data(kotzias_reviews_imdb)
#' @format A data frame with 1,041 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Kotzias Reviews: Yelp
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_yelp
#' @usage data(kotzias_reviews_yelp)
#' @format A data frame with 1,040 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Twitter Tweets About Self Driving Cars
#'
#' A dataset containing Twitter tweets about self driving cars, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_self_driving_cars
#' @usage data(crowdflower_self_driving_cars)
#' @format A data frame with 6,943 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Weather
#'
#' A dataset containing Twitter tweets about the weather, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_weather
#' @usage data(crowdflower_weather)
#' @format A data frame with 763 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Deflategate
#'
#' A dataset containing Twitter tweets about Tom Brady's deflated ball scandal,
#' taken from Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_deflategate
#' @usage data(crowdflower_deflategate)
#' @format A data frame with 11,786 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Products
#'
#' A dataset containing Twitter tweets about various products, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_products
#' @usage data(crowdflower_products)
#' @format A data frame with 3,548 rows and 2 variables
#' @references Cavender-Bares, K., (2013). Judge emotion about brands & products. \cr \cr
#' Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Apex AD2600 Progressive-scan DVD player Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Apex AD2600 Progressive-scan DVD player. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_apex_reviews
#' @usage data(hu_liu_apex_reviews)
#' @format A data frame with 740 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Cannon G3 Camera Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Cannon G3 Camera. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_cannon_reviews
#' @usage data(hu_liu_cannon_reviews)
#' @format A data frame with 597 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Creative Labs Nomad Jukebox Zen Xtra 40GB Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Creative Labs Nomad Jukebox Zen Xtra 40GB. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_jukebox_reviews
#' @usage data(hu_liu_jukebox_reviews)
#' @format A data frame with 1716 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Nikon Coolpix 4300 Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Nikon Coolpix 4300. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_nikon_reviews
#' @usage data(hu_liu_nikon_reviews)
#' @format A data frame with 346 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Nokia 6610 Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Nokia 6610. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_nokia_reviews
#' @usage data(hu_liu_nokia_reviews)
#' @format A data frame with 546 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
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#' Calculate Text Polarity Sentiment
#'
#' Calculate text polarity sentiment in the English language at the sentence
#' level and optionally aggregate by rows or grouping variable(s).
#' @docType package
#' @name sentimentr
#' @aliases sentimentr package-sentiment
NULL
#' Sam I Am Text
#'
#' A dataset containing a character vector of the text from Seuss's 'Sam I Am'.
#'
#' @docType data
#' @keywords datasets
#' @name sam_i_am
#' @usage data(sam_i_am)
#' @format A character vector with 169 elements
#' @references Seuss, Dr. (1960). Green Eggs and Ham.
NULL
#' 2012 U.S. Presidential Debates
#'
#' A dataset containing a cleaned version of all three presidential debates for
#' the 2012 election.
#'
#' @details
#' \itemize{
#' \item person. The speaker
#' \item tot. Turn of talk
#' \item dialogue. The words spoken
#' \item time. Variable indicating which of the three debates the dialogue is from
#' }
#'
#' @docType data
#' @keywords datasets
#' @name presidential_debates_2012
#' @usage data(presidential_debates_2012)
#' @format A data frame with 2912 rows and 4 variables
NULL
#' Movie Reviews
#'
#' A dataset containing sentiment scored movie reviews from a Kaggle competition
#' posted by University of Michigan SI650. The data was originally collected
#' from opinmind.com.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the review
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kaggle_movie_reviews
#' @usage data(kaggle_movie_reviews)
#' @format A data frame with 7,086 rows and 2 variables
#' @references Original URL: https://www.kaggle.com/c/si650winter11/data
NULL
#' Sentiment Scored New York Times Articles
#'
#' A dataset containing Hutto & Gilbert's (2014) sentiment scored New York Times
#' articles.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the article
#' }
#'
#' Vadar's Liscense:
#'
#' The MIT License (MIT)
#'
#' Copyright (c) 2016 C.J. Hutto
#'
#' Permission is hereby granted, free of charge, to any person obtaining a copy
#' of this software and associated documentation files (the "Software"), to deal
#' in the Software without restriction, including without limitation the rights
#' to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#' copies of the Software, and to permit persons to whom the Software is
#' furnished to do so, subject to the following conditions:
#'
#' The above copyright notice and this permission notice shall be included in all
#' copies or substantial portions of the Software.
#'
#' THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#' IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#' FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#' AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#' LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#' OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#' SOFTWARE.
#' @docType data
#' @keywords datasets
#' @name nyt_articles
#' @usage data(nyt_articles)
#' @format A data frame with 5,179 rows and 2 variables
#' @references
#' Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model
#' for Sentiment Analysis of Social Media Text. Eighth International Conference
#' on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
#'
#' Original URL: https://github.com/cjhutto/vaderSentiment
NULL
#' Student Course Evaluation Comments
#'
#' A dataset containing a subset of comments and rating from Welch & Mihalcea's
#' (2017) data set filtered to include comments with a one or more unambiguous
#' sentiment rating.
#'
#' @details
#' \itemize{
#' \item sentiment. A numeric sentiment score
#' \item text. The text from the evaluation
#' }
#'
#' @docType data
#' @keywords datasets
#' @name course_evaluations
#' @usage data(course_evaluations)
#' @format A data frame with 566 rows and 2 variables
#' @references Welch, C. and Mihalcea, R. (2017). Targeted sentiment to
#' understand student comments. In Proceedings of the International Conference
#' on Computational Linguistics (COLING 2016). \cr \cr
#' Original URL: http://web.eecs.umich.edu/~mihalcea/downloads.html#GroundedEmotions
NULL
#' Hotel Reviews
#'
#' A dataset containing a random sample (n = 5000 of 1,621,956) of Wang, Lu, &
#' Zhai's (2011) hotel reviews data set scraped by the authors from
#' Original URL: http://www.tripadvisor.com.
#'
#' @details
#' \itemize{
#' \item sentiment. The overall rating for the experience
#' \item text. The text review of the hotel
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hotel_reviews
#' @usage data(hotel_reviews)
#' @format A data frame with 5000 rows and 2 variables
#' @references Wang, H., Lu, Y., and Zhai, C. (2011). Latent aspect rating
#' analysis without aspect keyword supervision. In Proceedings of the 17th ACM
#' SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2011), 618-626. \cr \cr
#' Original URL: 'http://sifaka.cs.uiuc.edu/~wang296/Data/index.html'
NULL
#' Kotzias Reviews: Amazon Cells
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_amazon_cells
#' @usage data(kotzias_reviews_amazon_cells)
#' @format A data frame with 1,067 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Kotzias Reviews: IMBD
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_imdb
#' @usage data(kotzias_reviews_imdb)
#' @format A data frame with 1,041 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Kotzias Reviews: Yelp
#'
#' A dataset containing a list of 4 review data sets. Each data set contains
#' sentences with a positive (1) or negative review (-1) taken from reviews of
#' products, movies, & restaurants. The data, compiled by Kotzias, Denil, De Freitas,
#' & Smyth (2015), was originally taken from amazon.com, imdb.com, & yelp.com.
#' Kotzias et al. (2015) provide the following description in the README:
#' "For each website, there exist 500 positive and
#' 500 negative sentences. Those were selected randomly for larger datasets of
#' reviews. We attempted to select sentences that have a clearly positive or
#' negative connotation [sic], the goal was for no neutral sentences to be selected.
#' This data set has been manipulated from the original to be split apart by
#' element (sentence split). The original 0/1 metric has also been converted
#' to -1/1. Please cite Kotzias et al. (2015) if you reuse the data here.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the review.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name kotzias_reviews_yelp
#' @usage data(kotzias_reviews_yelp)
#' @format A data frame with 1,040 rows and 2 variables
#' @references Kotzias, D., Denil, M., De Freitas, N. & Smyth,P. (2015). From
#' group to individual labels using deep features. Proceedings of the 21th ACM
#' SIGKDD International Conference on Knowledge Discovery and Data Mining.
#' 597-606. Original URL: http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
NULL
#' Twitter Tweets About Self Driving Cars
#'
#' A dataset containing Twitter tweets about self driving cars, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_self_driving_cars
#' @usage data(crowdflower_self_driving_cars)
#' @format A data frame with 6,943 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Weather
#'
#' A dataset containing Twitter tweets about the weather, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_weather
#' @usage data(crowdflower_weather)
#' @format A data frame with 763 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Deflategate
#'
#' A dataset containing Twitter tweets about Tom Brady's deflated ball scandal,
#' taken from Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_deflategate
#' @usage data(crowdflower_deflategate)
#' @format A data frame with 11,786 rows and 2 variables
#' @references Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Twitter Tweets About the Products
#'
#' A dataset containing Twitter tweets about various products, taken from
#' Crowdflower.
#'
#' @details
#' \itemize{
#' \item sentiment. A human scoring of the text.
#' \item text. The sentences from the tweet.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name crowdflower_products
#' @usage data(crowdflower_products)
#' @format A data frame with 3,548 rows and 2 variables
#' @references Cavender-Bares, K., (2013). Judge emotion about brands & products. \cr \cr
#' Original URL: https://www.crowdflower.com/data-for-everyone
NULL
#' Apex AD2600 Progressive-scan DVD player Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Apex AD2600 Progressive-scan DVD player. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_apex_reviews
#' @usage data(hu_liu_apex_reviews)
#' @format A data frame with 740 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Cannon G3 Camera Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Cannon G3 Camera. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_cannon_reviews
#' @usage data(hu_liu_cannon_reviews)
#' @format A data frame with 597 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Creative Labs Nomad Jukebox Zen Xtra 40GB Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Creative Labs Nomad Jukebox Zen Xtra 40GB. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_jukebox_reviews
#' @usage data(hu_liu_jukebox_reviews)
#' @format A data frame with 1716 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Nikon Coolpix 4300 Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Nikon Coolpix 4300. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_nikon_reviews
#' @usage data(hu_liu_nikon_reviews)
#' @format A data frame with 346 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
#' Nokia 6610 Product Reviews From Amazon
#'
#' A dataset containing Amazon product reviews for the Nokia 6610. This
#' data set was compiled by Hu and Liu (2004). Where a sentence contains more
#' than one opinion score and average of all scores is used.
#'
#' @details
#' \itemize{
#' \item sentiment. Hu and Liu (2004)'s average opinion rating for a
#' sentence. Negative and positive reflects direction, a negative or positive
#' sentiment. Opinion strength varies between 3 (strongest), and 1 (weakest).
#' number. The review number.
#' \item text. The text from the review.
#' \item review_id. The review number.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name hu_liu_nokia_reviews
#' @usage data(hu_liu_nokia_reviews)
#' @format A data frame with 546 rows and 3 variables
#' @references
#' Minqing Hu and Bing Liu. (2004). Mining and summarizing customer reviews.
#' Proceedings of the ACM SIGKDD International Conference on
#' Knowledge Discovery & Data Mining (KDD-04).
#'
#' Minqing Hu and Bing Liu. (2004)."Mining Opinion Features in Customer
#' Reviews. Proceedings of Nineteeth National Conference on
#' Artificial Intelligence (AAAI-2004).
#'
#' Original URL: \file{https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html}
NULL
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