library(text2map)Concept Class Analysis (CoCA) is a method for grouping documents based on the schematic similarities in their engagement with multiple semantic directions (as measured in the previous section). This is a generalization of Correlational Class Analysis for survey data. We outline this method in more detail in our Sociological Science paper, “Concept Class Analysis: A Method for Identifying Cultural Schemas in Texts.”
The first step to use CoCA is build two or more semantic directions. For example, here are three semantic directions related to socio-economic status:
# build juxtaposed pairs for each semantic directions
pairs.01 <- data.frame(additions = c("rich", "richer", "affluence", "wealthy"),
substracts = c("poor", "poorer", "poverty", "impoverished") )
pairs.02 <- data.frame(additions = c("skilled", "competent", "proficient", "adept"),
substracts = c("unskilled", "incompetent", "inproficient", "inept") )
pairs.03 <- data.frame(additions = c("educated", "learned", "trained", "literate"),
substracts = c("uneducated", "unlearned", "untrained", "illiterate") )
# get the vectors for each direction
sd.01 <- get_direction(pairs.01, my.wv)
sd.02 <- get_direction(pairs.02, my.wv)
sd.03 <- get_direction(pairs.03, my.wv)
# row bind each direction
sem.dirs <- rbind(sd.01, sd.02, sd.03)Next, we feed our document-term matrix, word embeddings matrix, and
our semantic direction data.frame from above to the CoCA
function:
classes <- CoCA(my.dtm, wv = my.wv, directions = sem.dirs)
print(classes)Finally, using the plot() function, we can generate
simple visualizations of the schematic classes found:
# this is a quick plot.
# designate which module to plot with `module = `
plot(classes, module=1)