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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# twitterAnalysis
<!-- badges: start -->
[](https://github.com/UBC-MDS/twitterAnalysis/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
[](https://codecov.io/gh/UBC-MDS/twitterAnalysis)
The goal of twitterAnalysis is to assess a twitter user’s character based on their recent tweets
## Summary
Twitter is a popular social media app with over 1 billion user accounts. While a diversity of users is a strength, some individuals have concerns with the prevalence of “troll” accounts and individuals who exhibit unconstructive tone and diction whom they deem not worth engaging with. The package twitterAnalysis is intended to provide insight into a twitter user based on their tweet history in effort to determine if an account is worth engaging with. The package provides an easy to use interface for determining the general sentiment expressed by a user.
## Installation
You can install the development version of twitterAnalysis from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("UBC-MDS/twitterAnalysis")
```
## Example
Here is an example analysis using 300 recent tweets from a user
``` r
library(twitterAnalysis)
#retrieve 300 tweets from @elonmusk
tokens <- user_info("YOUR_consumer_key",
"YOUR_consumer_secret",
"YOUR_access_token",
"YOUR_access_token_secret")
elon_tweets <- load_twitter_by_user("elonmusk", 300, tokens)
#clean up tweets
cleaned_tweets <- generalPreprocessing(elon_tweets)
#label sentiment
labeled_tweets <- sentiment_labeler(cleaned_tweets, "text_clean")
#count frequency of each sentiment
sentiment_freq <- count_tweets(labeled_tweets)
#generate wordcloud
cleaned_tweets |>
clean_tweets() |>
count_words() |>
create_wordcloud()
```
## Functions
**load_twitter** : Returns a user's recent tweets (as a dataframe) given their `user id` using the Twitter API.
**generalPreprocessing** : Cleans the recent tweet dataframe generated by `load_twitter`. Includes features such as removing punctuation and extracting emojis.
**sentiment_labeler** : Determines the general (average) sentiment of recent tweets.
**count_tweets:** Return the proportion of three sentiments.
**create_wordcloud** : Generates a word cloud of most frequently used words in tweets.
## Scope and Fit
There are existing R packages that preform tweet analysis and preprocessing (including [textprepr](https://github.com/UBC-MDS/textprepr) and [tweetr](https://github.com/UBC-MDS/tweetr)). However, none of these packages focus of providing metrics in the context of determining if the twitter user is worth engaging with.
## Contributing
Interested in contributing? Check out the contributing guidelines in CONTRIBUTING.md. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
## License
`twitterAnalysis` was created by Andy Wang, Renzo Wijngaarden, Roan Raina, Yurui Feng. It is licensed under the terms of the MIT license.