actor_fetcher: removed from package other: major update to documentationmaster
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#' 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 ids 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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actor_merger <- function(df, actors_meta, ids = NULL) {
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grouper <- function(id, df) {
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return(df %>%
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rowwise() %>%
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filter(length(intersect(id,ids)) > 0) %>%
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group_by(`_id`) %>%
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summarise(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 = as.Date(first(`_source.publication_date`)),
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doctype = first(`_source.doctype`)) %>%
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mutate(
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ids = str_c(id, collapse = '-')
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)
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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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text_sent <- df %>%
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select(`_id`,starts_with("text."),-ends_with("sent_lemmas"))
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df <- df %>%
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select(-ends_with("sent_lemmas"),-starts_with("text.")) %>%
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unnest(cols = colnames(.)) ## Unnest to sentence level
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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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mutate(
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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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## Create aggregations according to list of actorId vectors in ids
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if(!is.null(ids)) {
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output <- lapply(ids,grouper, df = df) %>%
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bind_rows(.) %>%
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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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all <- df %>%
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rowwise() %>%
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filter(!is.null(unlist(ids))) %>%
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group_by(`_id`) %>%
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summarise(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 = as.Date(first(`_source.publication_date`)),
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doctype = first(`_source.doctype`)) %>%
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mutate(
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ids = "all"
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)
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df <- df %>%
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unnest(cols = ids) %>% ## Unnest to actor level
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mutate(
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`_source.publication_date` = as.Date(`_source.publication_date`)
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)
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## Create aggregate measures for individual actors
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actors <- df %>%
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filter(str_starts(ids,"A_")) %>%
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group_by(`_id`,ids) %>%
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summarise(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(`_source.publication_date`),
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doctype = first(`_source.doctype`)
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)
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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[-1128,]
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actors_meta_bydate <- actors_meta %>%
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mutate(
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startDate = as.Date(startDate),
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endDate = as.Date(endDate)
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) %>%
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select(
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lastName,firstName,`function`,gender,yearOfBirth,parlPeriod,partyId,ministerName,ministryId,actorId,startDate,endDate
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) %>%
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rowwise() %>%
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mutate(
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publication_date = list(seq(from=startDate, to=endDate,by="day")),
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ids = actorId
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) %>%
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unnest(cols=publication_date)
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## Join the actor metadata with the article data by actor id and date
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actors <- actors %>%
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left_join(.,actors_meta_bydate, by=c("ids","publication_date"))
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## Generate party-actor aggregations (mfsa)
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parties_actors <- df %>%
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filter(str_starts(ids,"P_")) %>%
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mutate(
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ids = str_sub(ids, start = 1, end = -3)
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) %>%
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group_by(`_id`,ids) %>%
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summarise(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(`_source.publication_date`),
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doctype = first(`_source.doctype`)) %>%
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mutate(
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ids = str_c(ids,"_mfsa")
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)
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## Generate party aggregations (mfs)
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parties <- df %>%
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filter(str_ends(ids,"_f") | str_ends(ids,"_s")) %>%
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mutate(
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ids = str_sub(ids, start = 1, end = -3)
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) %>%
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group_by(`_id`,ids) %>%
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summarise(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(`_source.publication_date`),
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doctype = first(`_source.doctype`)) %>%
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mutate(
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ids = str_c(ids,"_mfs")
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)
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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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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(df)
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}
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}
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@ -0,0 +1,24 @@
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/actor_merger.R
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\name{actor_merger}
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\alias{actor_merger}
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\title{Aggregate sentence-level dataset containing actors (from sentencizer())}
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\usage{
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actor_merger(df, actors_meta, ids = NULL)
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}
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\arguments{
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\item{df}{Data frame with actor ids, produced by sentencizer}
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\item{actors_meta}{Data frame containing actor metadata obtained using elasticizer(index="actors")}
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\item{ids}{Optional list of vectors, where each vector contains actor ids to be merged (e.g. merge all left-wing parties)}
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}
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\value{
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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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}
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\description{
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Aggregate sentence-level dataset containing actors (from sentencizer())
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}
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\examples{
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actor_merger(df, actors_meta, ids = NULL)
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}
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