STEP 2: CREATE DENDROGRAM or NETWORK
From your cleaned and formatted list, visualize relations between words
Option 1: Hierarchical Cluster Dendrogram
Words on any vector of words but only makes sense for unordered word
lists! Produces a dendogram from a vector of words. First pulls words,
then creates a square matrix with cosine distances for all possible word
pairs: d[i,j]. Then converts semantic distance matrix to Euclidean
distance. Then plots a hierchcial clustering solution moving words
closer together in proximity based on their distance.
Arguments:
dat dataframe processed using
clean_monologue_or_list()
output quoted
argument dendrogram or network default is
dendrogram
dist_type quoted argument,
which distance norms would you like? default is embedding
alt is ‘SD15’
Option 2: iGraph network
Takes hclust properties from dendrogram steps and creates a simple
igraph object.
dat dataframe cleaned using
clean_monologue_or_list
output quoted
argument dendrogram or network default is
dendrogram
dist_type default is
‘embedding’, alt is ‘SD15’
print(mynetwork)
#> IGRAPH 0167949 UNW- 17 68 --
#> + attr: name (v/c), cluster (v/n), color (v/c), size (v/n), label
#> | (v/c), label.color (v/c), label.cex (v/n), weight (e/n), color (e/c),
#> | width (e/n)
#> + edges from 0167949 (vertex names):
#> [1] trombone--missile trombone--gun trombone--bullet trombone--knife
#> [5] trombone--spear trombone--apple trombone--banana trombone--tomato
#> [9] trombone--disgust trombone--angry trombone--sad trombone--happy
#> [13] piano --missile piano --bullet piano --spear piano --banana
#> [17] piano --tomato piano --disgust piano --angry guitar --missile
#> [21] guitar --spear guitar --banana guitar --tomato guitar --disgust
#> + ... omitted several edges