2. Acknowledgement
¨ This analysis wouldn’t have been possible without the
support of SONIC @ Northwestern University and DELTA
Lab @ Georgia Tech in developing the project ‘My Dream
Team’.
¨ Other project members:
¤ Dr. Noshir Contractor, Northwestern University.
¤ Dr. Leslie DeChurch, Georgia Tech.
¤ Dr. Edward Palazzolo, Northwestern University.
¤ Harshad Gado, Northwestern University.
¤ Amy Wax, Georgia Tech.
¤ Samuel Posnock, Georgia Tech.
¤ Peter Seely, Georgia Tech.
3. Overview
¨ Description
¤ Objective
¤ Corpus details
¨ Methodology
¤ Different approaches
¤ Text Parsing
¤ Forming vectors
¤ Measuring similarity
¤ K-Means
¤ Choosing the optimum K
¤ Distribution & Density
¤ Topic Indicators
¨ Results
¤ What can we infer ?
¤ Supporting inference
¤ Gratitude & Request
¤ Need, Liking, Invite & Praise
¤ Collaboration & Grouping
¨ Conclusion
4. Project Description
¨ Objective
¤ To do textual analysis and find motivations behind message
interaction in the process of forming teams.
¤ Serve as a proof for some of the known reasons about team
formation ties, through, mathematically derived topical
patterns hidden in unstructured text data.
¨ Corpus details
¤ ‘My Dream Team’ run 19th – 23rd Feb, 2014.
¤ # Students participated : 214
¤ # Text messages exchanged : 353
¤ # Unique words/terms in entire corpus : 619
5. Methodology/Different Approaches
¨ Problem statement :
“ Given a text corpus X = { x1, x2, x3….} Where xi = document/message, find the topics/ideas
(in our context, primary motivations) that represent individual clusters within X.”
¨ Possible Approaches :
¤ Latent Semantic Analysis (mostly used in IR for indexing)
¤ Latent Dirichlet Allocation (probabilistic topic modeling)
¤ Document Clustering (we go by this)
¨ Problems :
¤ Real world textual data is always “dirty” when it comes to text parsing.
¤ Performance and accuracy can depend on rich vocabulary for
grammatical parsing.
6. Methodology/Text Parsing
“I enjoy learning and growing while also getting out a little of my competitive
spirit.”
“i enjoy learning and growing while also getting out a little of my competitive
spirit.”
[i, enjoy, learn, and, grow, while, also, get, out, a, little, of, my, competitive, spirit]
[i, enjoy, learn, grow, out, little, my, competitive, spirit]
Lowercase
Lemmatize – remove punctuations, split
into words & convert each word to its
root.
Remove Stopwords
7. Methodology/Forming Vectors
¨ Bag of words : Collect all unique words from the
corpus vocabulary.
¨ Document-Term index : For each word in the
vocabulary, count its frequency across all
documents/messages in the corpus. (example below)
Term/Word Message -1 Message-2 Message-3 Message-4
hey 1 1 1 0
team 1 1 0 1
join 1 0 1 1
work 1 0 1 0
8. Methodology/Measuring Similarity
¨ Cosine Similarity : Performs better when compared to
Euclidean measure. Similarity is mostly retained irrespective of
vectors space distance due to length of vectors. Intuitively,
documents/messages dealing with same topic/domain remain
close in vector space irrespective of their message length.
Euclidean distance
Cosine distance
Θ
x
y
(0,0)
Message 1
Message 2
Example : The figure on right indicates
2 message vectors. Although, the
Euclidean measure shows quite a bit of
distance in vector space, the Cosine
measure indicates that the vectors are
close enough to point in the same
direction. Cos 0 = 1 indicates similar
vectors, Cos 90 = 0 indicates dissimilar
vectors.
9. Methodology/K-Means
¨ The K-means algorithm is a method to automatically cluster similar data
examples together. The intuition behind K-means is an iterative procedure
that starts by guessing the initial centroids, and then refines this guess by
repeatedly assigning examples to their closest centroids and then
recomputing the centroids based on the assignments. Img source- Wikipedia
10. Methodology/Choosing the optimum K
In finding the optimum number of clusters/
means in the message corpus, we use the
‘Elbow Curve’ technique as shown in the
figure on right that plots the Jcost-min
function across number of means tried,
where,
Jcost-min = (1/m) Σ(xi – μci)2
m = number of messages.
xi = message vector.
ci = centroid number that vector xi
is assigned to.
μci= corresponding cluster centroid
vector to which xi belongs.
Thus, the number of optimum clusters
considered in MDT message corpus are 3
(The point where the graph curves like an
elbow).
11. Methodology/Distribution & Density
122
124
107
Message distribution among clusters
Cluster 1
Cluster 2
Cluster 3
0
200
400
600
800
1000
1200
1400
1600
1800
105 110 115 120 125
#Wordspercluster
# Messages per cluster
Cluster density
Cluster 3
The pie chart on the right gives the number of
messages distributed among 3 clusters in text
message corpus of MDT.
Cluster 2
Cluster 1
The graph on the right gives an idea of how
dense each cluster is with words or terms.
Note: Cluster-1 is bigger in size (has more
number of messages) when compared to
Cluster-3 but number of words that makeup
Cluster-1 is far less than that of Cluster-3.
This gives an important clue that Cluster-1 is
most likely madeup of ‘Short messages’.
12. Methodology/Topic indicators
On a broader scale we already know that all the
messages deal with ‘Team Formation’ topic. We are
on a hunt to find the hidden motivations on a sub-
topical scale.
The segmented pyramid on the right shows some of
the top ranking words by frequency in each cluster so
far with the core topic as ‘Team Formation’. The
words that are most common to all clusters and
reflect ‘Team Formation’ topic on a broader scale,
are at the core of the pyramid.
We consider top words as strong indicators of
hidden motivations.
Cluster-2
# messages : 124
# words : 1013
Cluster-3
# messages : 107
# words : 1601
Cluster-1
# messages : 122
# words : 387
13. Methodology/What can we infer ?
Cluster-1: Has short messages indicating gratitude or
request to be a member through words like ‘thanks’
and ‘accept’. Low frequency words from this cluster
are mostly a slang or non-dictionary word.
Cluster-2: Has messages that mostly refer recipient's
qualities and hence words like ‘cool’ & ‘like’ stand
out as some of the top words. Probably, these
messages also talk about sender's ‘need’ to add one
or more member to the team.
Cluster-3: Has messages that indicate topics such as
working together, grouping and mostly collaboration
with words like ‘group’, ‘work’ and ‘together’. with
high frequency.
Cluster-2
# messages : 124
# words : 1013
Cluster-3
# messages : 107
# words : 1601
Cluster-1
# messages : 122
# words : 387
14. Results/Supporting inference
¨ Though top words provide a strong indication of
probable topics in a cluster, high frequency of each
word isn’t enough to support our assumption of
topics.
¨ A good support to our inference would be through
a mathematical analysis of co-occurrence of the top
words from each cluster with the words ‘team’ and
‘you’ that makeup the core topic of ‘Team
Formation’.
15. Results/Probability for coherence
¨ Example :
“Ana (a word) is in the mall (topic) given her best friends Harry and Brian (‘team’ & ‘you’)
are in the mall (topic).”
¨ In other words, a word would define a topic only if it
co-occurs with other supporting words to reflect
coherence necessary to define that topic.
¨ P(topic) = P(w/X) where,
w = word, X = core words of topic ‘Team Formation’.
¨ We calculate P(topic) across all clusters for a given
word.
16. Results/Gratitude & Request by Cluster 1
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
thanks accept
Probability
words
Topic probability through conditional probability of words given core Team
Formation words ‘team’ and ‘you’
Cluster 1
Cluster 2
Cluster 3
17. Results/Need, Liking, Invite & Praise by Cluster 2
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
need one like join cool
Probability
words
Topic probability through conditional probability of words given core Team
Formation words ‘team’ and ‘you’
Cluster 1
Cluster 2
Cluster 3
18. Results/Collaboration & Grouping by Cluster 3
0
0.05
0.1
0.15
0.2
0.25
0.3
work group together
Probability
words
Topic probability through conditional probability of words given core Team
Formation words ‘team’ and ‘you’
Cluster 1
Cluster 2
Cluster 3
19. Conclusion
¨ Document clustering with probabilistic support to topical
inference over message corpus of MDT has helped us
expose following motivations behind sending messages.
Users interacted when,
¤ There was a need for one or more person to complete the team.
¤ They liked someone’s profile.
¤ They wanted to invite someone to join their team.
¤ They wanted to praise someone for good profile or looks.
¤ They wanted to group/merge their incomplete teams.
¤ They wanted to collaborate/work with someone.
¤ They wanted to express gratitude.
¤ They wanted to earnestly request someone to join.
