Sentiment Analysis of Twitter Data

Purpose

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.

Package

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.

Installation

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)

Using saotd

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.

Example

For an example of how to use this package, find the vignette at:

library(saotd)
utils::vignette("saotd")

Meta

• license:

  • All code is licensed GPL.
  • All data is from public data sources.

• Get citation information for saotd in R by running:

citation("saotd")

Getting help

If you encounter a clear bug, please file a minimal reproducible example on github.

Contributing

If you would like to contribute, please create a Pull Request and make appropriate applicable changes for review.

References

• AFIT Data Science Lab
• Tidyverse
• Text Mining with R
• ldatuning
• topicmodels