Nothing
#' 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
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.