senTWEETment

Inspired heavily by Text Mining with R, senTWEETment is a shiny app that helps you extract and conduct lexicon-based sentiment analysis on tweets in English. The design specification can be found in the design spec vignette.

This app is distributed as an R package under the golem framework. Under the hood, the main business logics are held in an R6 class, allowing for communications between modules.

Installation

You can install the latest version of senTWEETment from github with:

install.packages("remotes")
remotes::install_github("jiwanheo/senTWEETment")

Setup - lexicons

senTWEETment currently supports 3 pre-set lexicons libraries from textdata.(AFINN, Bing, NRC). To use them, users must explicitly agree to conditions to downloading/using these lexicons before launching the app for the first time. To do this, run in your console:

senTWEETment::get_lexicons()

Setup - Twitter API

The first time you launch senTWEETment, you’ll be prompted to provide a bearer token of your Twitter API app. Setting up the authorization can be found in the auth vignette.

Usage

To run the application from your local computer, run:

senTWEETment::run_app()

The app follows 3 straightforward steps that lead up to a text analysis output.

Step 1. Pull Tweets

On the left hand side, there are 3 inputs that you can search tweets with in one of 4 ways:

• Hashtag or text
• Location
• User
• Hashtag or text AND Location

Any input combinations outside these 4 are automatically disabled.

After choosing a method, you can provide further search options on the right hand side.

• Number of tweets is not guaranteed (tweets may have been deleted, modified, …)
• Include Retweets is defaulted to “Yes”, on user search.
• Pull By is defaulted to “Recent” on user search.

Press “Pull Tweets” to retrieve the tweets in a DT table with links to user profiles and tweets.

Step 2. Customize Analysis

In this step, you can specify which lexicons you’ll use for the analysis. You can choose between the 3 pre-set lexicons, or upload your own file.

You can also edit the list of negation words and stop words used in this analysis. Further explanation in the analysis vignette.

Once you’re happy with the settings of the analysis, press “Analyze!” to run the sentiment analysis.

Step 3. Analysis Result

The resulting analysis shows

• A summary of tallied-up/per-tweet sentiment
• Top 10 words with most impacts on analysis
• Original Tweets and their sentiments
• Contributing sentiment per word per tweet. (If blank, the tweet didn’t have any words with sentiments in the lexicon)