The document discusses network analysis, defining key concepts such as nodes and edges, and differentiating between directed and undirected networks. It outlines the process of analyzing text data, including cleaning, creating a term-document matrix, and plotting 3D and interactive visualizations of the network. Various techniques for visualizing and analyzing the connections between terms in the data are also presented.

1. Network
Analysis
Using 3D Interactive Plots

2. Network analysis
A network analysis is simply a collection of objects i.e. nodes or we can
also say vertices. And the connection between the nodes are called
edges or links.
Types of Network analysis
1) Undirected network: a connection
where all the edges are bidirectional
2) Directed network: a connection where all
the edges are directed from one end point to another.
A direction of the network are represented as arrows indicating the
direction to the nodes
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3. Network analysis
Degree of a network
In a undirected network, the network can have N number of different
connections from each nodes which is called as degree of network.
For example. Node 1 have 2 connections ,
node 2 have 3 connections.
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Node
Node 1
N 1.1
N 1.2
Node 2
N 2.1
N 2.2
N 2.3

4. Network analysis
#Network analysis now will help us to understand how many
connections a specific word has with other words.
#The keywords will be the “node”s in the network, and the
#Connections are will be called as edges
#read the data
reviews<-read.csv(file.choose(),stringsAsFactors=FALSE,nrows = 500)
View(reviews)
names(reviews)
reviews1<-data.frame(reviews$reviews.text)
names(reviews1)
dim(reviews1)
names(reviews1)[1]<-"reviews"
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5. Network analysis
#Build a Text Corpus
library(tm)
review.corpus<-Corpus(VectorSource(reviews1$reviews))
summary(review.corpus)
inspect(review.corpus[1:5]) #Inspecting elements in Corpus
#Data Transformations -Cleaning
#Converting to lower case
review.corpus<-tm_map(review.corpus,tolower)
#Removing extra white space
review.corpus<-tm_map(review.corpus,stripWhitespace)
#Removing punctuations
review.corpus<-tm_map(review.corpus,removePunctuation)
#Removing numbers
review.corpus<-tm_map(review.corpus,removeNumbers)
#Can add more words apart from standard list
my_stopwords<-c(stopwords('english'),'@','http*','url','www*')
review.corpus<-tm_map(review.corpus,removeWords,my_stopwords)
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6. Network analysis
library(igraph)
# Convert to tdm
reviews.tdm <-
TermDocumentMatrix(tweets.corpus,control=list(wordLengths=c(1, Inf)))
#Remove sparse terms
reviews.tdm.rm <- removeSparseTerms(reviews.tdm, sparse=0.95)
inspect(obama.tdm.rm[1:10,1:10])
#Transform the tdm to a matrix format.
reviews.m <- as.matrix(reviews.tdm.rm)
#convert the matrix values to 2 level factor 1/0(i.e. 'Yes' or 'No')
indicating 1 for existng values other than zero
reviews.m[reviews.m>=1] <- 1
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7. Network analysis
#Build Term Adjacency matrix which will show us how many
connections each TERM has.
#This is done by using the product of 2 matrices and the output will be
the number of times each term appears together in a document
reviews.m2 <- reviews.m %*% t(reviews.m)
reviews.m2[1:10,1:10]
#We can see the times each term/word occurred with other words.
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8. Network analysis
#Let's plot the adjacency matrix with a adjacency graph
#weight= "TRUE/False"(weight)
#mode="undirected"(bidirectional connection),"directed"(the edges are
directed from one end point to another)
reviews.graph <-
graph.adjacency(reviews.m2,weighted="TRUE",mode="directed")
library(igraph)
#Vertices/nodes(simply a collection of objects here its our keywords)
V(reviews.graph)
#Lists of connection between the nodes are called edges or links.
E(reviews.graph)
get.edgelist(reviews.graph) #will give the same output but in matrix format
#To check if weights are assigned
E(reviews.graph)$weight
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9. Network analysis
#Number of Connections for each word
degree(reviews.graph,loops = TRUE)
degree(reviews.graph,loops = FALSE)
#Lets try with mode= undirected (i.e. bidirectional)
reviews.graph <-
graph.adjacency(reviews.m2,weighted="TRUE",mode="undirected")
#Set the layout of the graph
layout1 <- layout.fruchterman.reingold(reviews.graph,dim=2)
layout2 <- layout_randomly(reviews.graph,dim=2)
?layout
plot(reviews.graph,
layout=layout1,
vertex.size=10,
vertex.label.color=“blue")
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10. Network analysis
#Remove Loops
reviews.g<-simplify(reviews.graph2)
#Vertices
V(reviews.g)
E(reviews.g)
#Number of edges & vertices
ecount(reviews.g)
vcount(reviews.g)
#List of connections
E(reviews.g)$weight
E(reviews.g)$weight <- runif(ecount(reviews.g))
#Number of Connections for each word
degree(reviews.g,mode="out",loops = TRUE)
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11. Network analysis
# plot the layout
layout1 <- layout.fruchterman.reingold(reviews.g,dim=3)
plot(reviews.g,layout=layout1, vertex.size=2, vertex.label.color="blue")
plot(reviews.g,layout=layout_with_graphopt)
plot(reviews.g,layout=layout_in_circle)
#Examples:component_wise, layout_as_bipartite, layout_as_star,
layout_as_tree, layout_in_circle, layout_nicely, layout_on_grid,
layout_on_sphere, layout_randomly, layout_with_dh, layout_with_fr,
layout_with_gem, layout_with_graphopt, layout_with_kk,
layout_with_lgl, layout_with_mds, layout_with_sugiyama, layout_,
merge_coords, norm_coords, normalize.
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12. Network analysis
#Few modifications to look the graph better
V(reviews.g)$label.cex <- 2 * V(reviews.g)$degree/
max(V(reviews.g)$degree)
V(reviews.g)$label.color <- rgb(0, 0, .2, .8)
V(reviews.g)$label.color <- "blue"
V(reviews.g)$frame.color <- NA
#Only if weights exist
edge_weight <- (-log(E(reviews.g)$weight)) / max(-
log(E(reviews.g)$weight))
E(reviews.g)$color <- rgb(0.3,0.5,0.9, edge_weight)
E(reviews.g)$width <- edge_weight
#E(reviews.g)$color<-"grey"
# plot with layout1
plot(reviews.g, layout=layout1,vertex.color="red")
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13. Network analysis
#using spinglass.community to plot the graph
#This function tries to find communities/groups in the graph/network via
a spin-glass model
#Spins refers to maximum number of groups that it can findspc <-
spinglass.community(reviews.g,spins=10)
Spc
plot(reviews.g, layout=layout1, vertex.size=.3,
vertex.label.cex=1.5,
edge.color=rgb(.4,.4,0,.3),
vertex.color=spc$membership+0.5,
vertex.label.color=spc$membership+0.5,
asp=FALSE)
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14. Network analysis
#----------------------------3D PLOTS -----------------------------------------#
V(reviews.g)$label <- V(reviews.g)$name
V(reviews.g)$degree <- degree(reviews.g)
library(rgl)
coords <- layout.kamada.kawai(reviews.g, dim=3)
open3d()
rglplot(reviews.g, vertex.size=5,edge.arrow.size=0.6,
layout=coords,
vertex.label.dist=0.5,
vertex.color="blue",
edge.color="green")
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15. Network analysis
#------------------------Interactive plots -------------------------------#
tkplot(reviews.g, layout=layout.kamada.kawai,
vertex.size=6,edge.arrow.size=0.6,vertex.label.dist=0.5,
vertex.color="blue",edge.color="green")
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16. Steps Network analysis
1) Building Text Corpus
2) Convert Corpus to tdm
3) Remove Spare terms
4) Transform TDM into a matrix
5) Convert to Boolen matrix
6) Build Adjacency Matrix
7) Build Adjacency graph
8) Remove Looks
9) Vertices, Edges, Degree
10) Assign Weights
11) Set Labels & degrees
12) Plot layout
13) Add more modification to plot properly
14) Only if weight exist modification
15) Plot layout
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