Converts a properly structured sentiment table into a sentiment object, that can be used for further aggregation with the aggregate.sentiment function. This allows to start from sentiment scores not necessarily computed with compute_sentiment.

as.sentiment(s)

Arguments

s

a data.table or data.frame that can be converted into a sentiment object. It should have at least an "id", a "date", a "word_count" and one sentiment scores column. If other column names are provided with a separating "--", the first part is considered the lexicon (or more generally, the sentiment computation method), and the second part the feature. For sentiment column names without any "--", a "dummyFeature" component is added.

Value

A sentiment object.

Examples

set.seed(505) data("usnews", package = "sentometrics") data("list_lexicons", package = "sentometrics") ids <- paste0("id", 1:200) dates <- sample(seq(as.Date("2015-01-01"), as.Date("2018-01-01"), by = "day"), 200, TRUE) word_count <- sample(150:850, 200, replace = TRUE) sent <- matrix(rnorm(200 * 8), nrow = 200) s1 <- s2 <- data.table::data.table(id = ids, date = dates, word_count = word_count, sent) s3 <- data.frame(id = ids, date = dates, word_count = word_count, sent, stringsAsFactors = FALSE) s4 <- compute_sentiment(usnews$texts[201:400], sento_lexicons(list_lexicons["GI_en"]), "counts", do.sentence = TRUE) m <- "method" colnames(s1)[-c(1:3)] <- paste0(m, 1:8) sent1 <- as.sentiment(s1) colnames(s2)[-c(1:3)] <- c(paste0(m, 1:4, "--", "feat1"), paste0(m, 1:4, "--", "feat2")) sent2 <- as.sentiment(s2) colnames(s3)[-c(1:3)] <- c(paste0(m, 1:3, "--", "feat1"), paste0(m, 1:3, "--", "feat2"), paste0(m, 4:5)) sent3 <- as.sentiment(s3) s4[, "date" := rep(dates, s4[, max(sentence_id), by = id][[2]])]
#> id sentence_id word_count GI_en date #> 1: text1 1 4 0 2016-02-05 #> 2: text1 2 30 0 2016-02-05 #> 3: text1 3 12 -1 2016-02-05 #> 4: text1 4 41 0 2016-02-05 #> 5: text1 5 34 0 2016-02-05 #> --- #> 2226: text200 8 22 1 2015-04-07 #> 2227: text200 9 17 1 2015-04-07 #> 2228: text200 10 28 2 2015-04-07 #> 2229: text200 11 20 0 2015-04-07 #> 2230: text200 12 7 1 2015-04-07
sent4 <- as.sentiment(s4) # further aggregation from then on is easy... sentMeas1 <- aggregate(sent1, ctr_agg(lag = 10)) sent5 <- aggregate(sent4, ctr_agg(howDocs = "proportional"), do.full = FALSE)
#> The choice 'lag = 1' implies no time aggregation, so we added a dummy weighting scheme 'dummyTime'.