PA_BotsWithIndiv.R

library(tidyverse)
library(jsonlite)
library(sentimentr)

#Change name of file as needed

file = "D:/Users/Kurt/Documents/Research/Dissertation/Data/tweets/01172020/economy01172020.json"


#Import file as a dataframe, limit to first 2000 tweets

current_day = fromJSON(file) %>% as.data.frame()

current_day = current_day[1:2000,]


#Select only the handles and the text of each tweet

handles_text = current_day %>% select(5, 6)


#Identify separate sentences in each tweet

tweet_text_sentences = get_sentences(handles_text$tweets.text)


#Score the sentiment of each sentence and then average them together to form a single sentiment score for each tweet

current_day_sentiment_scores = sentiment_by(tweet_text_sentences) %>%
  mutate(tweets.screen_name = handles_text$tweets.screen_name) %>% 
  select(-element_id)


#Reorder columns for readability

current_day_sentiment_scores = current_day_sentiment_scores[,c(4,3,2,1)]


#Calculate sentiment for the day's individuals

sentiment_by_individual = current_day_sentiment_scores %>% group_by(tweets.screen_name) %>%
  summarise(individual_sentiment = mean(ave_sentiment))

current_day_individual_sentiment = mean(sentiment_by_individual$individual_sentiment)

current_day_individual_sentiment