105 lines
3.8 KiB
R
105 lines
3.8 KiB
R
library(tidyverse)
|
|
|
|
neurobiber_csv <-"~/p2/quest/072325_biberplus_labels.csv"
|
|
neurobiber_df <- read.csv(neurobiber_csv , header = TRUE)
|
|
|
|
normalized_cols <- grep("^normalized_", names(neurobiber_df), value = TRUE)
|
|
|
|
neurobiber_df$normalized_features_vec <- lapply(
|
|
asplit(neurobiber_df[, normalized_cols], 1), as.numeric
|
|
)
|
|
|
|
X <- do.call(rbind, neurobiber_df$normalized_features_vec)
|
|
|
|
|
|
set.seed(808)
|
|
|
|
library(dplyr)
|
|
library(purrr)
|
|
table(neurobiber_df$source)
|
|
|
|
#neurobiber_df <- neurobiber_df %>%
|
|
# group_by(source) %>%
|
|
# mutate(cluster = {
|
|
# X_sub <- do.call(rbind, features_vec)
|
|
# as.factor(kmeans(X_sub, centers = 50)$cluster)
|
|
# }) %>%
|
|
# ungroup()
|
|
library(dbscan)
|
|
dbscan_result <- dbscan(X, eps = 0.5, minPts = 97)
|
|
neurobiber_df$dbcluster <- as.factor(ifelse(dbscan_result$cluster == -1, "Noise", dbscan_result$cluster))
|
|
|
|
kmeans_result <- kmeans(X, centers = 10)
|
|
neurobiber_df$cluster <- as.factor(kmeans_result$cluster)
|
|
table(neurobiber_df$dbcluster)
|
|
|
|
pca <- prcomp(X, center = TRUE, scale. = TRUE)
|
|
neurobiber_df$PC1 <- pca$x[,1]
|
|
neurobiber_df$PC2 <- pca$x[,2]
|
|
|
|
|
|
ggplot(neurobiber_df, aes(x = PC1, y = PC2, color = phase)) +
|
|
geom_point(size = 2, alpha = 0.7) +
|
|
theme_minimal() +
|
|
labs(title = "Across-case comment clusters (DBSCAN) by cross-case PCA",
|
|
x = "Principal Component 1",
|
|
y = "Principal Component 2") +
|
|
facet_wrap(~ source)
|
|
|
|
ggplot(neurobiber_df, aes(x = phase, y=dbcluster, fill=AuthorWMFAffil)) +
|
|
geom_violin(trim = FALSE, position = position_dodge(width = 0.8), alpha = 0.6) +
|
|
theme_minimal() +
|
|
labs(title = "Across-case comment clusters by feature deployment phase",
|
|
x = "Feature deployment phase",
|
|
y = "Neurobiber feature vector cluster (DBSCAN)") +
|
|
facet_wrap(~ source)
|
|
|
|
cluster_means <- aggregate(
|
|
X,
|
|
by = list(
|
|
WMFAffil = neurobiber_df$AuthorWMFAffil,
|
|
phase = neurobiber_df$phase,
|
|
comment_type = neurobiber_df$comment_type,
|
|
source= neurobiber_df$source
|
|
),
|
|
FUN = mean
|
|
)
|
|
|
|
rownames(cluster_means) <- apply(
|
|
cluster_means[, c("WMFAffil", "phase", "comment_type", "source")], 1,
|
|
function(x) paste(x, collapse = "_")
|
|
)
|
|
|
|
cluster_means <- cluster_means[, !(names(cluster_means) %in% c("WMFAffil", "phase", "comment_type", "source"))]
|
|
|
|
#cluster_means <- aggregate(X, by = list(Cluster = neurobiber_df$AuthorWMFAffil), FUN = mean)
|
|
#rownames(cluster_means) <- paste0("Cluster_", cluster_means$Cluster)
|
|
#cluster_means <- cluster_means[,-1] # Remove cluster label column
|
|
|
|
BIBER_FEATURES <- c(
|
|
"BIN_QUAN","BIN_QUPR","BIN_AMP","BIN_PASS","BIN_XX0","BIN_JJ",
|
|
"BIN_BEMA","BIN_CAUS","BIN_CONC","BIN_COND","BIN_CONJ","BIN_CONT",
|
|
"BIN_DPAR","BIN_DWNT","BIN_EX","BIN_FPP1","BIN_GER","BIN_RB",
|
|
"BIN_PIN","BIN_INPR","BIN_TO","BIN_NEMD","BIN_OSUB","BIN_PASTP",
|
|
"BIN_VBD","BIN_PHC","BIN_PIRE","BIN_PLACE","BIN_POMD","BIN_PRMD",
|
|
"BIN_WZPRES","BIN_VPRT","BIN_PRIV","BIN_PIT","BIN_PUBV","BIN_SPP2",
|
|
"BIN_SMP","BIN_SERE","BIN_STPR","BIN_SUAV","BIN_SYNE","BIN_TPP3",
|
|
"BIN_TIME","BIN_NOMZ","BIN_BYPA","BIN_PRED","BIN_TOBJ","BIN_TSUB",
|
|
"BIN_THVC","BIN_NN","BIN_DEMP","BIN_DEMO","BIN_WHQU","BIN_EMPH",
|
|
"BIN_HDG","BIN_WZPAST","BIN_THAC","BIN_PEAS","BIN_ANDC","BIN_PRESP",
|
|
"BIN_PROD","BIN_SPAU","BIN_SPIN","BIN_THATD","BIN_WHOBJ","BIN_WHSUB",
|
|
"BIN_WHCL","BIN_ART","BIN_AUXB","BIN_CAP","BIN_SCONJ","BIN_CCONJ",
|
|
"BIN_DET","BIN_EMOJ","BIN_EMOT","BIN_EXCL","BIN_HASH","BIN_INF",
|
|
"BIN_UH","BIN_NUM","BIN_LAUGH","BIN_PRP","BIN_PREP","BIN_NNP",
|
|
"BIN_QUES","BIN_QUOT","BIN_AT","BIN_SBJP","BIN_URL","BIN_WH",
|
|
"BIN_INDA","BIN_ACCU","BIN_PGAS","BIN_CMADJ","BIN_SPADJ","BIN_X"
|
|
)
|
|
BIBER_FEATURES_NO_BIN <- gsub("^BIN_", "", BIBER_FEATURES)
|
|
colnames(cluster_means) <- BIBER_FEATURES_NO_BIN
|
|
library(pheatmap)
|
|
pheatmap(cluster_means,
|
|
cluster_rows = FALSE, # Now features are clustered (rows)
|
|
cluster_cols = FALSE,
|
|
scale='none') # Standardize features
|
|
|