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Knowledge Architecture
Graphing Your Knowledge
David Meza
2019/04/09
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Purpose & Overview
Demonstrate a sample of text analysis techniques and approaches to getting value out of text
information
Background and Problems
Sentiment Analysis
Classification
Information Search & Extraction
10 / 31
Background
At the completion of each International Space Station (ISS) expedition, the ISS Program
office schedules a series of intensive debriefs with returning U.S. crew members and
International Partner crew members to determine individual observations and concerns
related to working and living on board the ISS.
An estimated 20 to 25 post­mission debriefs are conducted after each expedition; these
debriefs address topics related to specific disciplines and systems, including habitability and
human factors, logistics and maintenance, payloads, stowage, food, procedures, etc. (Schuh
et al. 2011)
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Problem Statement
The debriefs have resulted in approximately 80,000 crew comments, with each comment
averaging roughly 114 words; the equivalent of over 90 copies of Harper Lee's To Kill a
Mocking Bird. The quantity and raw text format makes human comprehension difficult to
answer questions such as:
How do the astronauts view various topics and systems?
What are the most important issues affecting space habitability?
How have perceptions on various topics and systems changed over time?
Can we automatically summarize content?
Can we search and extract specific content or recommendations?
14 / 31
Sentiment Theory
The algorithm utilizes a sentiment dictionary to tag polarized words. A context cluster   of
words is pulled from around this polarized word and are tagged as neutral  , negator  ,
amplifier  , or de­amplifier  . Each polarized word is then weighted by a constant value
(0.8) along with the number and position of the valence shifters directly surrounding the
positive or negative word. Last, these context clusters   are summed and divided by the
square root of the word count   yielding an unbounded polarity score  .
x
T
i
x
0
i
x
N
i
x
a
i
x
d
i
x
T
i
(√n) (δ)
δ =
x
T
i
√n
x
T
i
= ∑ ((1 + c(x
A
i
− x
D
i
)) ∗ w(−1)
∑ x
n
i
)
x
A
i
= ∑(wneg ⋅ x
a
i
)
x
D
i
= max(x
D
′
i
, −1)
x
D
′
i
= ∑(−wneg ⋅ x
a
i
+ x
d
i
)
wneg = (∑ x
N
i
) mod 2
15 / 31
Sentiment Theory
Sentence Word Demonstration Score
I love ISS polarized word 0.577
I hate ISS polarized word ­0.577
I barely love ISS de­amplifier & polarized word 0.100
I really love ISS amplifier & polarized word 0.900
I am the ISS neutral 0.000
I don't love ISS negator & polarized word ­0.500
I don't not love ISS negator & negator & polarized word 0.447
I love ISS and I hate mondays polarized word & polarized word 0.000
16 / 31
Sentiment Implementation
[[1]]
[1] ­0.3162278
[[2]]
[1] ­0.1924501
[[3]]
[1] 0.2041241
# Load libraries
library(qdap)
# Define a Comment
comment <­ c("Equipment malfunctions will also occur, particularly du
                 testing. In manned flight we must regard every malfu
                 observed peculiarity in the behavior or a system as 
                 disaster. Only when the cause is understood and a ch
                 made and verified, can we proceed with the flight pr
# Extract Sentences
sentences <­ sent_detect(comment)
# Apply polarity algorithm to each sentence
scores <­ lapply(sentences, function(x){polarity(x,
                polarity.frame = opshab.sent.dict)$group[,4]})
17 / 31
Sentiment Visualizations
"Equipment malfunctions will also occur, particularly during subsystem development testing.
In manned flight we must regard every malfunction, and, in fact, every observed peculiarity
in the behavior or a system as an important warning of potential disaster.
Only when the cause is understood and a change to eliminate it has been made and verified,
can we proceed with the flight program. "
­F.J. Bailey Jr. NASA
18 / 31
Chord Diagram
0
0
50
100
0
0
50
0
0
50
0
0
0
0
0
50
100
150
200
0
50
100
150
200
Procedures
Equipm
ent/Technology
Time/Schedule
Operations
Communication/Information
Stowage
Health/Safety
Environment
General
Personal/Relational
Positive
N
egative
Category
Comment Polarity
19 / 31
Box Plot
−1 −0.5 0 0.5 1
Personal/Relational
Time/Schedule
Procedures
General
Communication/Information
Equipment/Technology
Stowage
Operations
Environment
Health/Safety
Comment Polarity
20 / 31
Bubble Plot
9
7
4
35/36
33/34
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3
28/29
26/27
24/25
22/23
20/21
19/20
18
16/17
15
14
13
11
1
−1
−0.5
0
0.5
1
Mission
Comment Polarity
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Trends
2002 2004 2006 2008 2010 2012
­0.5
0.0
0.5
1.0
Communication/Information
­0.5
0.0
0.5
Comment.Polarity
Mission.Start
Comment.Polarity
22 / 31
Classification Theory
Consider the following two (short) documents:  
 = "I like ISS"  
 = "I hate ISS", 
 
one can construct a document­term matrix such as: 
Document I like hate ISS
1 1 0 1
1 0 1 1
 
Ward's method begins with all clusters as a singletons. A recursive algorithm is applied to
minimize the total within­cluster variance. The initial cluster distances in Ward's minimum
variance method are therefore defined to be the squared Euclidean distance between points:
X1
X2
X1
X2
dij = d({Xi}, {Xj}) = ∥Xi − Xj∥
2
23 / 31
Classification Implementation
# Load libraries
library(tm)
library(ape)
# Create Corpus
docs <­ Corpus(VectorSource(documents)) 
# Clean up documents
docs <­ tm_map(docs, content_transformer(tolower))
docs <­ tm_map(docs, removePunctuation)
docs <­ tm_map(docs, removeNumbers)
docs <­ tm_map(docs, removeWords, tm::stopwords("english"))
docs <­ tm_map(docs, stripWhitespace)
docs <­ tm_map(docs,stemDocument)
# Perform Clustering
dtm <­ DocumentTermMatrix(docs)
m <­ as.matrix(dtm)
d <­ dist(m)
groups <­ hclust(d, method="ward.D")
# plot
plot(as.phylo(groups), type="fan")
24 / 31
Classification Visualizations
25 / 31
Classification Result
When asked if the times the crewWhen asked if the times the crew experienced high noise levels on ISS were caused by on ISS were caused by
working behind the Service Module panels, one RSA crewmember indicated in the forwardworking behind the Service Module panels, one RSA crewmember indicated in the forward
end cone, where the SK­V is located.end cone, where the SK­V is located.In general you could say in the SM they hadIn general you could say in the SM they had noise
behind the SM panels, the forward cone in particular. , the forward cone in particular.  
When asked about theWhen asked about the WHC fan noise, the CSA crewmember said it was a little more like a, the CSA crewmember said it was a little more like a
resident tone. The one on orbit doesn't sound like that and it sounds more mechanical. The one on orbit doesn't sound like that and it sounds more mechanical. TheThe
separator made aseparator made a grinding noise and the one on Earth has more of a and the one on Earth has more of a hum to it.  to it. The transitionThe transition
sounds similar. sounds similar.  
Regarding Robonaut, the CSA crewmember is aware there is some sort ofRegarding Robonaut, the CSA crewmember is aware there is some sort of noise constraint
that they have to take it down because it has athat they have to take it down because it has a noise clock running, that is bogus. , that is bogus. TheThe
crewmember thinks Robonaut decreases thecrewmember thinks Robonaut decreases the noise in the Lab because it  because it absorbs sound; it is; it is
silent. If folks are worried about theIf folks are worried about the noise then there are a lot of other things they should then there are a lot of other things they should
worry about first. worry about first. That constraint should be removed, it is silly. That constraint should be removed, it is silly. The NASA crewmember saidThe NASA crewmember said
they couldn't tell when it was they couldn't tell when it was making noise.
26 / 31
Information Search &
Extraction Theory
Document I like hate ISS
1 1 0 1
1 0 1 1
We can then weight the documents
Where:   is the raw frequency of a term in a document
: total number of documents in the corpus
 is the number of documents where the term   appears
D1
D2
tfidf(t, d, D) = tf(t, d) ⋅ idf(t, D)
tf(t, d)
idf(t, D) = log[ ]
N
1 + |d ∈ D : t ∈ d|
N
N = | D |
|d ∈ D : t ∈ d| t
27 / 31
Once the weightings have been established, we can compare the similarity of the vectors in
the document term vector space. The way we compare the similarity of the vectors is by
using the cosine similarity
 where   and   are components of vectors   and cos(θ) = =
→A⋅
→B
||
→A|| || →B||
∑
n
i=1
AiBi
√∑
n
i=1
A
2
i
√∑
n
i=1
B
2
i
Ai Bi
→A →B
28 / 31
Information Search &
Extraction Implementation
# Load Libraries
#devtools::install_github﴾"daftmath/orion", force=TRUE﴿
library(orion)
# Create weighted document term matrix
mat <­ orion_prep(COMMENT)
# Calculate cosine similarity between search vector and documents
result <­ orion_search(mat, "nutrition")
# Store results sorted by ranking
df <­ arrange(data.frame(COMMENT, result), ­result)
29 / 31
Information Search &
Extraction Result
When asked if the US When asked if the US food warmer heated the heated the food adequately, the RSA crewmember saidadequately, the RSA crewmember said
yes.yes. 
 
When asked how long it took to heat theWhen asked how long it took to heat the food in the US in the US food warmer, the RSA crewmember, the RSA crewmember
said about 15 to 20 minutes.said about 15 to 20 minutes. 
 
The CSA crewmember said the The CSA crewmember said the food was great. The crew  The crew ate well and tried toand tried to eat everything..
The crew tried to find the oldest The crew tried to find the oldest food on station andon station and eat that first. Overall, the CSAthat first. Overall, the CSA
crewmember was delighted with what was onboard forcrewmember was delighted with what was onboard for food. There was variety and lots of. There was variety and lots of
good quality items and it was just really impressive so the crew was very happy with thegood quality items and it was just really impressive so the crew was very happy with the
food.
30 / 31
Thank You For Your Time!
Questions????
@davidmeza1
Bryant, C., Schuh, S., Schoenstein, N., & Meza, D. (2017). Understanding the International
Space Station Crew Perspective Following Long Duration Missions Through Analytics and
Visualization of Crew Feedback. In Advances in Usability and User Experience (Vol. 607,
pp. 74–86). Springer International Publishing.
31 / 31

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