1. Analyzing the Enron Emails using Topical Analysis and Graph Theory
Casey Kalinowski
Faculty Mentors: Dr. Zakaria Kurdi & Dr. Kim McCabe

2. Statement of the Problem
The Enron Scandal
2001 Bankruptcy of the Enron Corporation
Investors lost millions of dollars
Investigations by the FBI, IRS, and the Securities and Exchange Commission lasting for 5 years
The Enron s
520,914 s from over 150 employees of the Enron Corporation
Obtained during the investigation and released to the public as the largest dataset of its kind
Stress how 520,914 s is way too many to be able to go through during an investigation without aid from software
Is it possible to analyze the s with modern techniques in a way that is
more efficient and effective than the original investigation?

3. Application of Theory Topical Analysis Graph Theory
Artificial Intelligence
Natural Language Processing
Create topics and assign a topic to each piece of data
Graph Theory
Application of Graphs to research
Graph – Nodes connected by Edges
Node Degree – number of edges coming from a node
Edge Weight – numerical value shared by the two connected nodes
Say that topical analysis in the criminology world is content analysis

4. Application of Theory Topical Analysis Models Creating the Graphs
Gensim
Latent Dirichlet Allocation Model
Natural Language ToolKit (NLTK)
WordNet
Keyword Search
Nodes – Unique address
Node degree – Number of edges connecting to other addresses
Edges – between two nodes
Edge weight – Number of s sent between two nodes

5. Methodology Research Design Source of Data Test Sample
Retrospective - relies on previously collected data
Exploratory - little or no previous research on this topic
Source of Data
520,914 s from 156 employees of the Enron Corporation
Collected during Enron Investigation and released to the public by the Federal Energy Regulatory Commission in 2004
Retrieved from Carnegie Mellon University
Test Sample
5890 s from inbox/outbox of Kenneth Lay
It is ongoing research, that is why the test sample is so small

6. Analysis of the Data Processing the data Creating the topics
Topical Analysis of the data
Creating the graphs
Analyzing the graphs

7. Processing the Data
Converting the s from .CSV Format to .JSON format with Python
.CSV
"dasovich-j/notes_inbox/526.", "Message-ID:
Date: Wed, 20 Sep :51: (PDT)
From:
To:
Subject:
Cc:
Mime-Version: 1.0
Content-Type: text/plain; charset=us-ascii
Content-Transfer-Encoding: 7bit
Bcc:
X-From: Steven J Kean
X-To: David Parquet
X-cc: Jeff Dasovich, Sandra McCubbin
X-bcc:
X-Folder: \Jeff_Dasovich_Dec2000\Notes Folders\Notes inbox
X-Origin: DASOVICH-J
X-FileName: jdasovic.nsf
I talked to Hettie today. It's unlikely that we are going to find time for Jeff and the Governor to talk (because of the Governor's schedule). We’ll try to set something up later. In the meantime, the Governor should just sign the bill. Of course, Hettie had already communicated this; the Gov’s office acknowledged that the message was received but did not make a specific commitment. "
.JSON
{‘owner’:’dasovich-j’, ‘date’:’ :51:00’, ‘subject’:’ ‘, ‘message’:’ I talked to Hettie today. It's unlikely that we are going to find time for Jeff and the Governor to talk (because of the Governor's schedule). We’ll try to set something up later. In the meantime, the Governor should just sign the bill. Of course, Hettie had already communicated this; the Gov’s office acknowledged that the message was received but did not make a specific commitment.’}
Show differences between the two formats

8. Creating the Topics NLTK WordNet and Keyword Search Picked by hand
Accounting, Bankruptcy, Fraud, Leisure, Management, Meeting, Stock, and None
Mention that Gensim is artificial inteligence

9. Creating the Topics LDA Model
“I talked to Hettie today. It's unlikely that we are going to find time for Jeff and the Governor to talk (because of the Governor's schedule). We’ll try to set something up later. In the meantime, the Governor should just sign the bill. Of course, Hettie had already communicated this; the Gov’s office acknowledged that the message was received but did not make a specific commitment.”
Gensim - Latent Dirichlet Allocation (LDA)
Create training set from only the Nouns and Plural Nouns from s
Feed the training set to Gensim to create LDA Model
Process s with LDA Model to create lists of topics
Use the top six topics and use them with the WordNet technique
Business, Employees, Information, Market, People, and Stock
“today time governor talk schedule try set something meantime governor sign bill course office message commitment”
LDA Model s
Topic 0: talk, bill, office, commitment
Topic 1: governor, course, office, today
Topic 2: talk, schedule, message, set

10. Topical Analysis of the Data
Keyword Search
Very simple and easy to implement
Keyword with highest number of occurrences in an becomes that ’s topic

11. Topical Analysis of the Data
NLTK WordNet
Use WordNet to find the hyponyms of every topic
Compare every hyponym of every topic to every sense of every word in each of the s
Use WordNet to find similarity between hyponym and sense of the word
Similarity of .25 or higher gets scored 1 point
The topic with the highest weighted average score becomes the topic of the
Weighted Average = (Total score / Total number of word senses in the )
If similarity is .25 or higher, A point is added

12. Topical Analysis of the Data
Tested on 50 random s
Manually assign topics to each
Run topical analysis on the s
Compare the generated topics to the manually assigned topics

13. Creating the Graphs NetworkX – Python tool Load email from .JSON file
Create a node for each unique address
Create edge between sending node and receiving node and +1 to edge weight
Export as .graphml file

14. Analyzing the Graphs
Gephi – Open source graph analysis and visualization tool
View graph data (node degree, edge weight, centrality, etc.)
Visualize the graphs
Most importantly, refine the graphs

15. Analyzing the Graphs All topics 31 nodes, 38 edges
Nodes sized by degree
Edges sized by weight
Note the size of the node

16. Analyzing the Graphs Gensim + WordNet Topic Market 26 nodes 32 edges
Talk about how rosalee flemming connects to ken lay

17. Analyzing the Graphs WordNet Topic Accounting 18 nodes 20 edges
Note the edge between and
Talk about how, as an investigator, I would look into these other people

18. Limitations to Research
Reliability of the data
Did not collect the Enron s personally
No record of what was done to the s before being released to the public
But this is not truly a limitation because this is more of a concept and test than real research

19. Conclusion and Future Proposals
Work in progress
Improvements to effectiveness and efficiency
Promising Results
WordNet and Gensim higher accuracy than Keyword Search
Future Application
Once refined, technique can be applied to other datasets

20. References
10 Enron Players: Where They Landed After the Fall. (2006, January 29). The New York Times. Retrieved January 25, 2018, from after-the-fall.html Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks.International AAAI Conference on Weblogs and Social Media. Cohen, W. W. (2015, May 8). Enron Dataset. Retrieved December 12, 2017, from Famous Cases and Criminals: Enron. (2016, July 20). Retrieved January 20, 2018, from Federal Energy Regulatory Commission. (n.d.). The Western Energy Crisis, the Enron Bankruptcy, and FERC’s Response. Retrieved January 25, 2018, from Klein, B. (n.d.). Python Advanced: Graphs in Python. Retrieved January 29, 2018, from Moon, B., Mccluskey, J. D., & Mccluskey, C. P. (2010). A general theory of crime and computer crime: An empirical test. Journal of Criminal Justice, 38(4), doi: /j.jcrimjus Peixin Zhao, Marjorie Darrah, Jim Nolan, & Cun-Quan Zhang. (2014). Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study. The Scientific World Journal, doi: /2014/ Python Software Foundation. Python Language Reference, version Available at Ruohonen, K. (2013). Graph Theory (J. Tamminen, K. Lee, & R. Piché, Trans.). Retrieved January 27, 2018, from Salkind, N. J. (2010). Encyclopedia of Research Design. Thousand Oaks, Calif: SAGE Publications, Inc.