In systats/textlearnR:

knitr::opts_chunk$set(echo = TRUE)

Paper Github

• count number of CrowdFlower users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable by CF).
• hate_speech number of CF users who judged the tweet to be hate speech.
• offensive_language = number of CF users who judged the tweet to be offensive.
• neither = number of CF users who judged the tweet to be neither offensive nor non-offensive.
• class = class label for majority of CF users. 0 - hate speech 1 - offensive language 2 - neither

You must create a model which predicts a probability of each type of toxicity for each comment.

pacman::p_load(tidyverse)
hate_dat <- read_csv("labeled_data.csv") %>% 
  rename(id = X1) %>%
  glimpse

# write_rds(toxic_dat, path = "toxic_dat.rds")
# save(toxic_dat, file = "hate_dat")

systats/textlearnR documentation built on May 6, 2019, 8:31 p.m.