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198 lines
8.7 KiB
198 lines
8.7 KiB
#' Aggregate sentence-level dataset containing actors (from sentencizer())
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#'
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#' Aggregate sentence-level dataset containing actors (from sentencizer())
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#' @param df Data frame with actor ids, produced by sentencizer
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#' @param actors_meta Data frame containing actor metadata obtained using elasticizer(index="actors")
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#' @param actor_groups Optional list of vectors, where each vector contains actor ids to be merged (e.g. merge all left-wing parties)
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#' @return When no ids, returns actor-article dataset with individual actors, party aggregations, party-actor aggregations and overall actor sentiment (regardless of specific actors). When ids, returns aggregations for each vector in list
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#' @export
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#' @examples
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#' actor_merger(df, actors_meta, ids = NULL)
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#################################################################################################
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#################################### Generate actor-article dataset #############################
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#################################################################################################
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### NOTE: The exceptions for various partyId_a ids has been implemented because of an error with
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### some individual actors, where the partyId of an individual actor doesn't match an actual
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### partyId in the actor dataset
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actor_merger <- function(df, actors_meta, actor_groups = NULL) {
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grouper <- function(id2, df) {
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if ('P_1206_a' %in% id2) {
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id2 <- c('P_212_a','P_1771_a',id2)
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}
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if ('P_1605_a' %in% id2) {
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id2 <- c('P_1606_a', id2)
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}
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if ('P_1629_a' %in% id2) {
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id2 <- c(str_c('P_',as.character(1630:1647),'_a'), id2)
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}
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return(df[ids %in% id2,] %>%
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.[!duplicated(.,by = c('id','sentence_id')),.(
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actor.sent = sum(sent_sum)/sum(words),
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actor.sent_sum = sum(sent_sum),
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actor.sent_words = sum(sent_words),
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actor.words = sum(words),
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actor.arousal = sum(sent_words)/sum(words),
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actor.first = first(sentence_id),
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actor.occ = .N,
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publication_date = first(publication_date),
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ids = str_c(id2, collapse = '-')
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), by = c('id')]
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)
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}
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## Remove some of the metadata from the source df
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df <- data.table(df)[,.(
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(.SD),
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doctype = as.factor(`_source.doctype`),
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publication_date = as.Date(`_source.publication_date`),
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id = as.factor(`_id`)
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), .SDcols = !c('_source.doctype','_source.publication_date','_id')]
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text_sent <- df[,.SD, .SDcols = c('id', 'doctype',grep('text\\.',names(df), value = T))]
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## Create bogus variables if sentiment is not scored
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if(!"sent_sum" %in% colnames(df)) {
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df <- df[,.(
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(.SD),
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sent_words = 0,
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sent_sum = 0
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)]
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}
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## Unnest to sentence level
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df <- df[,lapply(.SD, unlist, recursive=F),
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.SDcols = c('sentence_id', 'sent_sum', 'words', 'sent_words','ids'),
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by = list(id,publication_date)]
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text_noactors <- df[lengths(ids) == 0L,
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.(noactor.sent = sum(sent_sum)/sum(words),
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noactor.sent_sum = sum(sent_sum),
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noactor.sent_words = sum(sent_words),
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noactor.words = sum(words),
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noactor.arousal = sum(sent_words)/sum(words),
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noactor.first = first(sentence_id),
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noactor.occ = .N), by = list(id)]
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all <- df[lengths(ids) > 0L,
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.(actor.sent = sum(sent_sum)/sum(words),
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actor.sent_sum = sum(sent_sum),
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actor.sent_words = sum(sent_words),
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actor.words = sum(words),
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actor.arousal = sum(sent_words)/sum(words),
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actor.first = first(sentence_id),
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actor.occ = .N,
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ids = 'all'), by = list(id)]
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## Unnest to actor level
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df <- df[,.(ids = as.character(unlist(ids))),
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by = list(id,publication_date,sentence_id, sent_sum, words, sent_words)]
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## Create aggregations according to list of actorId vectors in ids
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if(!is.null(actor_groups)) {
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output <- lapply(actor_groups,grouper, df = df) %>%
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rbindlist(.) %>%
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left_join(text_sent, by="id") %>%
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mutate(
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actor.prom = actor.occ/text.sentences,
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actor.rel_first = 1-(actor.first/text.sentences),
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year = strftime(publication_date, format = '%Y'),
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yearmonth = strftime(publication_date, format = '%Y%m'),
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yearmonthday = strftime(publication_date, format = '%Y%m%d'),
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yearweek = strftime(publication_date, format = "%Y%V")
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)
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return(output)
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} else {
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## Create aggregate measures for individual actors
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actors <- df[str_starts(ids, 'A_'),
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.(actor.sent = sum(sent_sum)/sum(words),
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actor.sent_sum = sum(sent_sum),
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actor.sent_words = sum(sent_words),
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actor.words = sum(words),
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actor.arousal = sum(sent_words)/sum(words),
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actor.first = first(sentence_id),
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actor.occ = .N,
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publication_date = first(publication_date)), by = list(id, ids)]
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## Create actor metadata dataframe per active date (one row per day per actor)
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colnames(actors_meta) <- str_replace(colnames(actors_meta),'_source.','')
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actors_meta <- actors_meta[,
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.((.SD),
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startDate = as.Date(startDate),
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endDate = as.Date(endDate),
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ids = ifelse(actorId != '', actorId, partyId)
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), .SDcols = -c('_id','startDate','endDate','_index','_type','_score')
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]
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actors <- actors_meta[actors,
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c('x.startDate','x.endDate',colnames(actors), 'lastName','firstName','function.','gender','yearOfBirth','parlPeriod','partyId','ministerName','ministryId','actorId','startDate','endDate'),
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on =.(ids = ids, startDate <= publication_date, endDate >= publication_date),
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allow.cartesian = T,
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mult = 'all',
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with = F][,.(
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startDate = x.startDate,
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endDate = x.endDate,
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(.SD)
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), .SDcols = -c('x.startDate', 'x.endDate','startDate','endDate')]
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## Generate party-actor aggregations (mfsa)
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# identical(as.data.frame(setcolorder(setorderv(parties_actors,c('id','ids')), colnames(parties_actors_dp))),as.data.frame(parties_actors_dp))
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parties_actors <- df[str_starts(ids,'P_'),.(
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ids = str_sub(ids, start = 1, end = -3),
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(.SD)
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),.SDcols = -c('ids')][, .(
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ids = case_when(ids == 'P_212' ~ 'P_1206',
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ids == 'P_1771' ~ 'P_1206',
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ids == 'P_1606' ~ 'P_1605',
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ids %in% str_c('P_',as.character(1630:1647)) ~ 'P_1629',
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TRUE ~ ids),
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(.SD)
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), .SDcols = -c('ids')][,.(
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actor.sent = sum(sent_sum)/sum(words),
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actor.sent_sum = sum(sent_sum),
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actor.sent_words = sum(sent_words),
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actor.words = sum(words),
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actor.arousal = sum(sent_words)/sum(words),
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actor.first = first(sentence_id),
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actor.occ = .N
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), by = c('id','ids')]
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parties_actors <- actors_meta[parties_actors, on = c('ids'), mult = 'first'][!is.na(id),.(ids = str_c(ids,"_mfsa"), (.SD)), .SDcols = -c('ids')]
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## Generate party aggregations (mfs)
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parties <- df[str_ends(ids,'_f') | str_ends(ids,'_s'),.(
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ids = str_sub(ids, start = 1, end = -3),
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(.SD)
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),.SDcols = -c('ids')][,.(
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actor.sent = sum(sent_sum)/sum(words),
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actor.sent_sum = sum(sent_sum),
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actor.sent_words = sum(sent_words),
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actor.words = sum(words),
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actor.arousal = sum(sent_words)/sum(words),
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actor.first = first(sentence_id),
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actor.occ = .N
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), by = c('id','ids')]
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parties <- actors_meta[parties, on = c('ids'), mult = 'first'][!is.na(id),.(ids = str_c(ids,"_mfs"), (.SD)), .SDcols = -c('ids')]
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## Join all aggregations into a single data frame, compute derived actor-level measures, and add date dummies
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df <- bind_rows(actors, parties, parties_actors, all) %>%
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left_join(.,text_sent, by="id") %>%
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left_join(.,text_noactors, by="id") %>%
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mutate(
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actor.prom = actor.occ/text.sentences,
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actor.rel_first = 1-(actor.first/text.sentences),
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year = strftime(publication_date, format = '%Y'),
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yearmonth = strftime(publication_date, format = '%Y%m'),
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yearmonthday = strftime(publication_date, format = '%Y%m%d'),
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yearweek = strftime(publication_date, format = "%Y%V")
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) %>%
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ungroup() %>%
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select(-contains('Search'),-starts_with('not'))
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return(df)
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}
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}
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