cat.vader

Social media content classification with LLMs. A domain wrapper around cat.stack that adds social media sourcing parameters for classifying posts from Reddit, Twitter/X, YouTube, and other platforms.

cat.vader wraps the Python catvader package via reticulate.

Installation

# From R-universe (recommended)
install.packages("cat.vader",
                 repos = c("https://chrissoria.r-universe.dev",
                          "https://cloud.r-project.org"))

# Or from a local clone
devtools::install("path/to/cat.stack")
devtools::install("path/to/cat.vader")

# Install the Python backend (one-time setup)
# pip install cat-vader

Quick Start

Classify social media posts

library(cat.vader)

# Fetch and classify Reddit posts
results <- classify(
  categories     = c("Political", "Economic", "Social", "Other"),
  sm_source      = "reddit",
  sm_handle      = "politics",
  sm_limit       = 100L,
  sm_months      = 3L,
  api_key        = Sys.getenv("OPENAI_API_KEY"),
  sm_credentials = list(
    client_id     = Sys.getenv("REDDIT_CLIENT_ID"),
    client_secret = Sys.getenv("REDDIT_CLIENT_SECRET")
  )
)

# Classify pre-loaded data
results <- classify(
  input_data  = df$posts,
  categories  = c("Positive", "Negative", "Neutral"),
  description = "Social media sentiment",
  api_key     = Sys.getenv("OPENAI_API_KEY")
)

Extract categories

result <- extract(
  sm_source      = "reddit",
  sm_handle      = "technology",
  sm_limit       = 200L,
  api_key        = Sys.getenv("OPENAI_API_KEY"),
  sm_credentials = list(
    client_id     = Sys.getenv("REDDIT_CLIENT_ID"),
    client_secret = Sys.getenv("REDDIT_CLIENT_SECRET")
  )
)
print(result$top_categories)

Functions

Function	Description
classify()	Classify social media content into categories
extract()	Discover and extract categories from social media data
explore()	Get raw category extractions for saturation analysis

License

MIT