http://www.forum.santini.se * The Quest: finding the optimal way to handle Big Textual Data for Information Discovery * The Question: is R convenient for text analytics of Big TEXTUAL Data? * Mission: identification of pros, cons, limits, benefits … Current Status: investigation in progress…

1. Text Analytics and R
Open Question: A Good Match?
Marina Santini
(LinkedIn)
Research Scientist at SICS East Swedish ICT AB (Santa Anna)
R useR MeetUp: Text analytics using R
R useR group (StockholmR)
R useR MeetUp, 14 March 2013, 18:00
Stockholm

2. My Quest or… Why do I attend this meetup?
– The Quest: finding the optimal way to handle Big
Textual Data for Information Discovery
– The Question: is R convenient for text analytics of
Big TEXTUAL Data?
– Mission: identification of pros, cons, limits,
benefits …
• Current Status: investigation in progress…
R useR MeetUp, 14 March 2013, 18:00
Stockholm

3. Outline
• Big Data vs. Big TEXTUAL Data
• Text Analytics & NLP (Natural Language Processing)
• Statistics for linguistics with R by Stefan Th. Gries
• From Information Discovery to Actionable
TEXTUAL Intelligence
• The Enron Challange: Predictions and Crisis
Intelligence
R useR MeetUp, 14 March 2013, 18:00
Stockholm

4. Big Data
• BIG DATA [Wikipedia]:
– Big data usually includes data sets with sizes beyond the ability of
commonly used software tools to capture, curate, manage, and
process the data within a tolerable elapsed time. Big data sizes are a
constantly moving target, as of 2012 ranging from a few dozen
terabytes to many petabytes of data in a single data set. With this
difficulty, new platforms of "big data" tools are being developed to
handle various aspects of large quantities of data.
– Examples include Big Science, web logs, RFID, sensor networks, social
networks, social data (due to the social data revolution), Internet
text and documents, Internet search indexing, call detail records,
astronomy, atmospheric science, genomics, biogeochemical,
biological, and other complex and often interdisciplinary scientific
research, military surveillance, medical records, photography archives,
video archives, and large-scale e-commerce.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

5. R, Strata, Hadoop…?
Apparently many solutions are available on
the market…
Uhm… Big Data is a vague label…
R useR MeetUp, 14 March 2013, 18:00
Stockholm

6. Merrill Lynch is one of the world's
Big Unstructured leading financial management and
advisory companies, providing
TEXTUAL Data financial advice and investment
banking services.
“Merrill Lynch estimates that more than 85 percent of all
business information exists as unstructured data –commonly
appearing in e‐mails, memos, notes from call centers and
support operations, news, user groups, chats, reports, letters,
surveys, white papers, marketing material, research,
presentations and web pages.” [DM Review Magazine,
February 2003 Issue]
 ECONOMIC LOSS!
A plethora of diverse document genres!
R useR MeetUp, 14 March 2013, 18:00
Stockholm

7. Simple search is not enough…
• Of course, it is possible to use simple search. But
simple search is unrewarding, because is based on
single terms.
– ”a search is made on the term felony. In a simple search,
the term felony is used, and everywhere there is a
reference to felony, a hit to an unstructured document is
made. But a simple search is crude. It does not find
references to crime, arson, murder, embezzlement,
vehicular homicide, and such, even though these crimes
are types of felonies” * Source: Inmon, B. & A. Nesavich,
"Unstructured Textual Data in the Organization" from
"Managing Unstructured data in the organization",
Prentice Hall 2008, pp. 1–13]
R useR MeetUp, 14 March 2013, 18:00
Stockholm

8. Textual Documents and Document Genres
R useR MeetUp, 14 March 2013, 18:00
Stockholm

9. Definition: Text Analytics
• A set of NLP techniques that provide some
structure to textual documents and help
identify and extract important information.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

10. Set of NLP techniques
• Common components of a text analytic
package are:
– Tokenization
– Morphological Analysis
– Syntactic Analysis
– Named Entity Recognition
– Sentiment Analysis
– Automatic Summarization
– Etc.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

11. NLP at Coursera
R useR MeetUp, 14 March 2013, 18:00
Stockholm

12. NLP is pervasive
Ex: spell-checkers
• Google Search
• Google Mail
• Facebook
• Office Word
• *…+
R useR MeetUp, 14 March 2013, 18:00
Stockholm

13. NLP is parvasive
Ex: Name Entity Recognition
• Opinion
mining
• Brand Trends
• Conversation
clouds on web
magazines and
online
newspapers…
R useR MeetUp, 14 March 2013, 18:00
Stockholm

14. Sentiment Analysis
R useR MeetUp, 14 March 2013, 18:00
Stockholm

15. Text Analytics Products and Frameworks
• Commercial Products: Open Source Frameworks:
– Attensity • GATE
– Clarabridge • NLTK
– Temis • UIMA
– Lexalytics • etc.
– Texify
– SAS
– SPSS
– IBM Cognos
– etc.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

16. However… (I)
• NLP tools and applications (both commercial
and open source) are not perfert. Research is
still very active in all NLP subfields.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

17. Ex: Syntactic Parser
• Connexor
• What about parsing a tweet?
• “My son, 6y/o, asked me for the first time today how
my DAY was . . . I about melted. Told him that I had
pizza for lunch. Response? No fair “ (Twitter Tutorial 1:
How to Tweet Well)
R useR MeetUp, 14 March 2013, 18:00
Stockholm

18. Why NLP and Text Analytics are
important for Information Discovery?
• Why is it important to know that a word is a noun, or a
verb or the name of brand?
• Broadly speaking:
• Nouns and verbs: Nouns are important for topic
detection; verbs are important if you want to identify
actions or intentions.
• Adjectives = sentiment identification.
• Function words (a.k.a. stop words) are important for
authorship attribution, plagiarism detection, etc.
• etc.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

19. However… (II)
• At present, the main pitfall of many NLP applications is
that they are not flexible enough to:
– Completly disambiguate language
– Identify how language is used in different types of
documents (a.k.a. genres).
For instance, in tweets langauge is used in a different
way than an emails, language used in email is
different from the language used in academic papers,
etc. )
• Often tweaking NLP tools to different types of text or
solve language ambiguity in an ad-hoc manner is
time-consuming, difficult and unrewarding…
R useR MeetUp, 14 March 2013, 18:00
Stockholm

20. How can R help?
• Can R help overcome NLP shortcomings and
open a new direction in Text Analytics and
Information Discovery in order to extract
useful information from Big TEXTUAL Data?
R useR MeetUp, 14 March 2013, 18:00
Stockholm

21. Existing literature for linguists
• Stefan Th. Gries (2013) Statistics for linguistics
With R: A Practical Introduction. De Gruyter
Mouton. New Edition.
• Stefan Th. Gries (2009) Quantitative corpus
linguistics with R: a practical introduction.
Routledge, Taylor & Francis Group (companion
website).
• Harald R. Baayen (2800) Analyzing Linguistic Data:
A Practical Introduction to Statistics using R.
Cambridge.
• ….
R useR MeetUp, 14 March 2013, 18:00
Stockholm

22. Companion website by Stefan Th. Gries
• BNC=British National Corpus (PoS tagged)
R useR MeetUp, 14 March 2013, 18:00
Stockholm

23. BNC
• The British National Corpus (BNC) is a 100 million word collection of
samples of written and spoken language from a wide range of sources,
designed to represent a wide cross-section of British English from the later
part of the 20th century, both spoken and written. The latest edition is
the BNC XML Edition, released in 2007.
• The corpus is encoded according to the Guidelines of the Text Encoding
Initiative (TEI) to represent both the output from CLAWS (automatic part-
of-speech tagger) and a variety of other structural properties of texts (e.g.
headings, paragraphs, lists etc.). Full classification, contextual and
bibliographic information is also included with each text in the form of a
TEI-conformant header.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

24. R & the BNC: Excerpt from Google Books
R = Corpus-based Lingusitc Analysis = OK
1. Descriptive statistics
2. Analytical statistics
3. Multifactorial methods
R useR MeetUp, 14 March 2013, 18:00
Stockholm

25. What about Information Discovery?
• Non standardized language
• Non standard texts
• Electronic documents of all kinds, eg. formal,
informal, short, long, private, public, etc.
R useR MeetUp, 14 March 2013, 18:00
Stockholm

26. Information Discovery 
Actionable Textual Intelligence
• Business Intelligence (BI) + Customer Analytics +
Social Network Analytics + Crisis Intelligence *…+ =
Actionable Textual Intelligence
• Actionable Textual Intelligence is information that:
1. must be accurate and verifiable
2. must be timely
3. must be comprehensive
4. must be comprehensible
5. !!! give the power to make decisions and to act straightaway !!!
6. !!! must handle BIG BIG BIG UNSTRUCTURED TEXTUAL DATA !!!
R useR MeetUp, 14 March 2013, 18:00
Stockholm

27. From The Economist:
The Big Data scenario
R useR MeetUp, 14 March 2013, 18:00
Stockholm

28. Enron & Crisis Intelligence:
The Enron Scandal
• The Enron scandal, revealed in October 2001, eventually
led to the bankruptcy of the Enron Corporation, an
American energy company based in Houston, Texas.
• “Enron's complex financial statements were confusing to
shareholders and analysts. In addition, its complex business
model and unethical practices required that the company
use accounting limitations to misrepresent earnings and
modify the balance sheet to indicate favorable
performance. According to McLean and Elkind in their
book The Smartest Guys in the Room, "The Enron scandal
grew out of a steady accumulation of habits and values and
actions that began years before and finally spiraled out of
control. “ *wikipedia]
R useR MeetUp, 14 March 2013, 18:00
Stockholm

29. The Enron Dataset
http://www.cs.cmu.edu/~enron/
• ” This dataset was collected and prepared by the
CALO Project (A Cognitive Assistant that Learns
and Organizes). It contains data from about 150
users, mostly senior management of Enron,
organized into folders. The corpus contains a total
of about 0.5M messages. This data was originally
made public, and posted to the web, by the
Federal Energy Regulatory Commission during its
investigation.”
• Resource for researchers
R useR MeetUp, 14 March 2013, 18:00
Stockholm

30. The Challenge: Crisis Intelligence
• Task:
Can you suggest and implement a predictive model
that would tell us that the Enron CRISIS (= scandal &
collapse) would have happend by analysing and
processing the raw textual data of emails belonging
to the Enron dataset with R?
Some basic references:
•Enron scandal at-a-glance, BBC
•The Enron Dataset (corpus=dataset=document collection)
•A subset of about 1700 labeled email messages (4.5M ) [genre, topic,
emotion]
•Actionable Corpus & Actionable Intelligence (this post contains
additional referenes in the cmments)
R useR MeetUp, 14 March 2013, 18:00
Stockholm

31. Thank you for your attention
Preseantation available here:
http://www.slideshare.net/marinasantini1/text-analytics-and-r
http://www.forum.santini.se/
R useR MeetUp, 14 March 2013, 18:00
Stockholm