This package is focused on utilizing Twitter data due to its widespread global acceptance. The rapid expansion and acceptance of social media has opened doors into opinions and perceptions that were never as accessible as they are with today’s prevalence of mobile technology. Harvested Twitter data, analyzed for opinions and sentiment can provide powerful insight into a population. This insight can assist companies by letting them better understand their target population. The knowledge gained can also enable governments to better understand a population so they can make more informed decisions for that population. During the course of this research, data was acquired through the Public Twitter Application Programming Interface (API), to obtain Tweets as the foundation of data and will build a methodology utilizing a topic modeling and lexicographical approach to analyze the sentiment and opinions of text in English to determine a general sentiment such as positive or negative. The more people express themselves on social media, this application can be use1`d to gauge the general feeling of people.
The saotd package is an R interface to the Twitter API
and can be used to acquire Tweets based on user selected #hashtags and
was developed utilizing a tidyverse approach. The package was designed
to allow a user to conduct a complete analysis with the contained
functions. The package will clean and tidy the Twitter data, determine
the latent topics within the Tweets utilizing Latent Dirichlet
Allocation (LDA), determine a sentiment score using the Bing lexicon
dictionary and output visualizations.
You can install the CRAN version using:
install.packages("saotd")You can install the development version from GitHub using:
install.packages("devtools")
devtools::install_github('evan-l-munson/saotd', build_vignettes = TRUE)The functions that are provided by saotd are broken down
into five different categories: Acquire, Explore, Topic Analysis,
Sentiment Calculation, and Visualizations.
Acquire
tweet_acquire allows a user to acquire Tweets of their
choosing by accessing the Twitter API. In order to do this the user
needs to have a Twitter account.
Additionally once the user has an account they will then need to sign up
for a Twitter
Developers account. Once a user has a Twitter developers account and
has received their individual consumer key, consumer secret key, access
token, and access secret key, they can acquire Tweets based on a list of
hashtags and a requested number of entries per hashtag.Explore
tweet_tidy removes all emoticons, punctuation,
weblinks, etc and converts converts the data to a tidy structure.merge_terms merges terms within a dataframe and
prevents redundancy in the analysis.unigram displays the text Uni-Grams within the Twitter
data in sequence from the most used to the least used. A Uni-Gram is a
single word.bigram displays the text Bi-Grams within the Twitter
data in sequence from the most used to the least used. A Bi-Gram is a
combination of two consecutive words.trigram displays the text Tri-Grams within the Twitter
data in sequence from the most used to the least used. A Tri-Gram is a
combination of three consecutive words.bigram_network Bi-Gram networks builds on computed
Bi-Grams. Bi-Gram networks serve as a visualization tool that displays
the relationships between the words simultaneously as opposed to a
tabular display of Bi-Gram words.word_corr displays the word correlation between
words.word_corr_network displays the mutual relationship
between words. The correlation network shows higher correlations with a
thicker and darker edge color.Topic Analysis
number_topics determines the optimal number of Latent
topics within a dataframe by tuning the Latent Dirichlet Allocation
(LDA) model parameters. Uses the ldatuning package and
outputs an ldatuning plot. This process can be time consuming
depending on the size of the dataframe.tweet_topics determines the Latent topics within a
dataframe by using Latent Dirichlet Allocation (LDA) model parameters.
Uses the ldatuning package and outputs an ldatuning plot.
Prepares Tweet text, creates DTM, conducts LDA, display data terms
associated with each topic.Sentiment Calculation
tweet_scores calculates the Sentiment Scores using the
Bing
Lexicon Dictionary that will account for sentiment by hashtag or
topic.posneg_words determines and displays the most positive
and negative words within the Twitter data.tweet_min_scores determines the minimum scores for
either the entire dataset or the minimum scores associated with a
hashtag or topic analysis.tweet_max_scores determines the maximum scores for
either the entire dataset or the maximum scores associated with a
hashtag or topic analysis.Visualizations
tweet_corpus_distribution determines the scores
distribution for the entire Twitter data corpus.tweet_distribution determines the scores distribution
by hashtag or topic for Twitter data.tweet_box displays the distribution scores of either
hashtag or topic Twitter data.tweet_violin displays the distribution scores of either
hashtag or topic Twitter data.tweet_time displays how the Twitter data sentiment
scores through time.tweet_worldmap function is not longer exported, as the
Twitter data does not contain latitude and longitude values. Displays
the location of a Tweet across the globe by hashtag or topic.For an example of how to use this package, find the vignette at:
library(saotd)
utils::vignette("saotd")license:
Get citation information for saotd in R by
running:
citation("saotd")If you encounter a clear bug, please file a minimal reproducible example on github.
If you would like to contribute, please create a Pull Request and make appropriate applicable changes for review.