capturing the value of unstructured data: introduction to text mining
Transcript of capturing the value of unstructured data: introduction to text mining
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CAPTURING THE VALUE OF UNSTRUCTURED DATA: INTRODUCTION TO TEXT MINING
Mary-Elizabeth (“M-E”) EddlestonePrincipal Systems Engineer, AnalyticsSAS Customer Loyalty, SAS Institute, Inc.
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2Copyright © 2013, SAS Institute Inc. All rights reserved.
Is there valuable information “locked away” in your unstructured data?
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CURRENT SITUATION: COMMON QUESTIONS ABOUT TEXTUAL DATA SOURCES
How can I leverage on our textual data sources?
What value can it bring?
Are there hidden insights within text datasources that can help my organization?
Such as call center notes, emails, news, government filings, social media…
How can I leverage on both unstructured and structured
data sources?Customer data + Customer
feedback?
Need to leverage the most from text data!
Can I also use text data to analyze and
predict the future?To reduce fraud, reduce churn, improve sales, reduce costs…
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WHAT IF YOU COULD….
Extract key information from text data? e.g. people, places, companies
See how things are related to each other?
Across a large number of documents and messages?
Discover main ideas/ topics across all documents and messages
Find patterns across non/text data, that can predict the future
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WHAT IF YOU COULD…
Discover new insights from large text data sources
Extract key patterns from text data to predict the future
Discover current topics about your products from customer opinions
Find patterns within customer feedback, that predicts good interest in upsell
opportunities
Detect anomalies from usual topics described in text reports,
text applications or feedback
Find patterns in reports that may seem to predict/ relate to suspicious behavior
Understand previously unknown issues/ concerns, from citizen discussions on
twitter/ forums
Extract key opinions from citizen feedback to forecast citizen sentiments
in the near future
Customers
Fraud
Public Opinion
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Text Mining has numerous applications in any industry
WHERE IS TEXT MINING USED?
GovernmentDetect fraudulent activity. Spot emerging trends and
public concerns.
FinanceRetention of current customer
base using call center transcriptions or transcribed
audio. Identification of potentially fraudulent activities.
InsuranceIdentify fraudulent claims.
Track competitive intelligence.
Brand management
Life SciencesIdentify adverse
events.Recommend
appropriate research materials.
Manufacturing
Reduce time to detect root cause of product issues.Identify trends in market
segments.
TelecommunicationsHelp prevent churn and suggest
up-sell/cross-sell opportunities for individual customers.
RetailIdentify the most profitable
customers and the underlying reasons for their
loyalty.Brand management
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TEXT MINING
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SAS® Text Analytics
Domain-Driven
Information Organization and Access
SAS Enterprise
Content Categorization
SAS Ontology
Management
Analysis-Driven
Predictive Modeling, Discover Trends and Patterns
SAS Text Miner SAS Sentiment Analysis
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SAS® TEXT MINER • Is a complete solution, to discover insights or predict behaviour and outcomes – by leveraging on data mining capabilities of SAS®
Enterprise Miner™ and SAS natural language processing (NLP)/ advanced linguistic technologies.
• What is Concept Extraction?
• To automatically locate and extract the key information from documents based on the rules & advanced linguistic logic
• What is Concept Linking?
• To look within a large corpus of text documents to discover how concepts/ key information are associated/ linked with each other.
• What is Topic Discovery?
• To analyse a large corpus of text documents to discover topics by grouping messages that has very similar content.
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HOW DOES TEXT MINING WORK? EXPLORING & DISCOVERING INSIGHTS
1. Input text messages –e.g. twitter data, reports,
email, news, forum messages
3. Discover Topics – cluster documents of similar content
and describe them with important key words
2. Parse & explore Text Data –break down text and explore relationships of key concepts such as persons,
places, organizations…
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HOW DOES TEXT MINING WORK? DISCOVER PATTERNS FOR PREDICTIVE MODELING
1. Input text messages with relevant structured
data –e.g. email, call center notes, applications
Customer data
2. Parse Text Data and Discover Topics – Break down text into
structured data, group messages of similar content
3. Predictive Modeling with text data – text data input into models may provide reliable info to predict
outcome & behavior
Predict activity that is likely fraudulent…
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WHAT CAN WE DISCOVER?Discover relationships between concepts described in large
corpus of text data –how are persons, places, organizations related?
Discover topics mentioned in text data–what are main topics mentioned?
What are the rare topics?
Discover patterns related to structured data –
e.g. how is feedback related to customer purchase behavior?
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EXAMPLE – DISCOVERING INSIGHTS FROM CUSTOMER COMPLAINT DATA
This is even more difficult when we wish to detect concepts and patterns within the documents, in order to find trends and detect
high risk events
How can we analyse millions of documents quickly and identify key patterns and cases of high risk? (e.g. risk of fraudulent activity)
From customer complaints to engineer logs to legal documents, it is a considerable challenge to draw insights from large amounts
of information, and usually unfeasible via manual means.
THE DRIVER SIDE SEAT BELT SOMETIMES FAILS TO RETRACT. WHEN I PULLED THE BELT OUT, IT STAYED OUT AND WOULD NOT RETRACT. I INSPECTED THE AREA AND FOUND NO INTERFERENCE. THIS
HAPPENED ON A SAT. I DROVE THE VEHICLE SAT. AND SUN WITH A FAULTY BELT. I CALLED THE DEALERS SERVICE DEPT. TOLD THEM THE PROBLEM BUT
COULDN'T GET IN FOR A WEEK.
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EXAMPLE – DISCOVERING INSIGHTS FROM CUSTOMER COMPLAINT DATA
SAS Text Miner automates manual comprehension of text documents, uncovering relationships and trends of concepts mentioned across documents, allowing drill down analysis and integrated with predictive modeling
within SAS Enterprise Miner.
In this example, we look at a large database of car faults
Car Fault Records
THE DRIVER SIDE SEAT BELT SOMETIMES FAILS TO RETRACT.
WHEN I PULLED THE BELT OUT, IT STAYED OUT AND WOULD NOT
RETRACT. I INSPECTED THE AREA AND FOUND NO INTERFERENCE…
Here, SAS Text Miner runs a Text Parsing processing on thousands of reports of car faults –• Recognizing and extracting entities and parts of speech • Supporting a wide range of languages • Into a detailed term/ document matrix• Allowing us deeper analysis/ visualization of insights
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EXAMPLE – DISCOVERING INSIGHTS FROM CUSTOMER COMPLAINT DATA
This allows us to discover relationships between concepts across all messages –
e.g. what is usually mentioned with issues such as “brake problems”?
Discover topics mentioned in text data– e.g.Understand the main topics: “dealerships”…
Uncover the emerging topics: “Battery issues”…
Discover patterns related to structured data –e.g. Complaints on “engine trouble” have a
higher chance of car accidents
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EXAMPLE – DISCOVERING INSIGHTS FROM CUSTOMER COMPLAINT DATA
• Discovery of new insights/ topics:
• Text data – forum messages, emails, logs, records typically contain rich, yet sparse/ uncommon insights.
• Text mining allows you to:• Parse and extract information
from text data • Reliably filter and retain
important information• Automatically group documents
into similar topics, allowing discovery of important/ large topics or rare/ small topics
• Text mining input in Predictive modeling:
• Documents and records often contain important facts that can reliably predict outcomes – for e.g. any mention of bad maintenance habits will likely result in earlier car failure
• Empowered by SAS Natural Language Processing and wide multi‐language support, Text mining discovers key trends within large amounts of text, to be used as clean, reliable input in data mining analysis.
How does this help?
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BENEFITS
• SAS Text Miner helps your organization to: Uncover previously undetected associations and relationships
Get a complete view data, and drill down to specific documents for more insight
Automate time-consuming tasks of reading and understanding text.
Analyse both text and non-text data produce predictive models that spot more opportunities and recognize trends more accurately
Discover hidden patterns from text data for insights and predictive modeling!
Discover hidden patterns from text data for insights and predictive modeling!
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SAS® TEXT MINER
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SAS® TEXT MINER – ANALYTICAL WORKFLOW
Text Mining
Raw Data Model with Structured and Unstructured Data
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EXAMPLE TEXT MINING PROCESS FLOWS
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EXAMPLE TEXT MINING PROCESS FLOWS
Start with a table that contains either:- Documents saved as a variable (column)- A column that points to physical text files
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EXAMPLE INPUT DATA VARIABLE CONTAINS FULL TEXT
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EXAMPLE INPUT DATA VARIABLE CONTAINS POINTER TO TEXT FILE
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EXAMPLE TEXT MINING PROCESS FLOWS
Apply natural language processing algorithms to parse the documents and quantify information about the terms in the corpus.
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TEXT PARSING NODE
• Tokenization - break sentences or documents into terms • Stemming - identify the root form of a word (run, runs, running, ran,
etc.)• Synonyms • Remove low-information words such as a, an, and the (stop list)• Part of speech identification (noun, verb, etc.)• Identify Standard and Custom Entities (names, places, etc.) Multiword terms or phrases (“blue screen of death”) Import custom entities, facts, and events as defined in SAS Enterprise Content
Categorization (ECC) Include negation entities from SAS ECC for Sentiment Analysis
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SUPPORTED LANGUAGES
Arabic, Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Polish, Portuguese, Spanish, and Swedish, Czech, Danish, Finnish, Greek, Hebrew, Hungarian, Indonesian, Norwegian, Romanian, Russian, Slovak, Thai, Turkish, Vietnamese, Russian, Greek, Vietnamese, Turkish, Czech, Indonesian, Thai, Danish, Norwegian, Slovak, Finnish, Romanian, Hebrew, Hungarian, Korean
New in SAS 9.3
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EXAMPLE TEXT MINING PROCESS FLOWS
Perform spell-checking and refine synonym lists. Discover related concepts using Concept Linking. Perform full text search. Subset documents and/or terms for further analysis.
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TEXT FILTER NODE
• Spell checking• Concept Linking• Full text search• Define additional synonyms• Sub-setting management of terms and documents that are
passed to subsequent nodes
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FILTER VIEWER
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SAS
Text
Min
ing
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CONCEPT LINKING
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EXAMPLE TEXT MINING PROCESS FLOWS
Analyze the documents to create topics and assign each document to one or more topics. In addition to derived topics, users can add their own topic definitions.
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TEXT TOPIC NODE
• Multiple topics per document• Soft clustering using rotated SVD (PROC SVD followed by
PROC FACTOR)• Allows automatic creation of single and multi-word topics• User defined topics and editing of automatic topics
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INTERACTIVE TOPIC VIEWER
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EXAMPLE TEXT MINING PROCESS FLOWS
Analyze the documents to create clusters and assign each document to a single cluster.
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CLUSTER VIEWER
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CLUSTER VIEWER
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EXAMPLE TEXT MINING PROCESS FLOWS
Clusters can be further explored using the Segment Profile node to identify factors that differentiate data segments from the population.
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SEGMENT PROFILE
• The Segment Profile node is available on the Assess tab of Enterprise Miner.
• It allows the examination of segmented or clustered data to identify factors that differentiate data segments from the population.
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SEGMENT PROFILE
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EXAMPLE TEXT MINING PROCESS FLOWS: PREDICTION
Several methods are available to use the unstructured data to create predictions.
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Text Mining has numerous applications in any industry
WHERE IS TEXT MINING USED?
GovernmentDetect fraudulent activity. Spot emerging trends and
public concerns.
FinanceRetention of current customer
base using call center transcriptions or transcribed
audio. Identification of potentially fraudulent activities.
InsuranceIdentify fraudulent claims.
Track competitive intelligence.
Brand management
Life SciencesIdentify adverse
events.Recommend
appropriate research materials.
Manufacturing
Reduce time to detect root cause of product issues.Identify trends in market
segments.
TelecommunicationsHelp prevent churn and suggest
up-sell/cross-sell opportunities for individual customers.
RetailIdentify the most profitable
customers and the underlying reasons for their
loyalty.Brand management
![Page 43: capturing the value of unstructured data: introduction to text mining](https://reader034.fdocuments.in/reader034/viewer/2022051712/586e0c3a1a28ab20708b6bcd/html5/thumbnails/43.jpg)
Copyr igh t © 2013, SAS Ins t i tu te Inc . A l l r igh ts reserved.
BENEFITS
• SAS Text Miner helps your organization to: Uncover previously undetected associations and relationships
Get a complete view data, and drill down to specific documents for more insight
Automate time-consuming tasks of reading and understanding text.
Analyse both text and non-text data produce predictive models that spot more opportunities and recognize trends more accurately
Discover hidden patterns from text data for insights and predictive modeling!
Discover hidden patterns from text data for insights and predictive modeling!
![Page 44: capturing the value of unstructured data: introduction to text mining](https://reader034.fdocuments.in/reader034/viewer/2022051712/586e0c3a1a28ab20708b6bcd/html5/thumbnails/44.jpg)
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LEARNING MORE
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SAS® TEXT MINER RESOURCES
SAS Text Miner Product Web Sitehttp://www.sas.com/text-analytics/text-miner/index.html
SAS Text Miner Technical Support Web Sitehttp://support.sas.com/software/products/txtminer/index.html
SAS Text Miner Technical Forum (Join Today!)https://communities.sas.com/community/support-communities/sas_data_mining_and_text_mining
SAS TrainingData Miner Training Path: http://support.sas.com/training/us/paths/dm.htmlCourses for SAS® Text Miner: https://support.sas.com/edu/prodcourses.html?code=TM&ctry=US
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http://support.sas.com/documentation/onlinedoc/txtminer/index.html
Step-by-step
how-toguide
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Data for the step-by-
step how-toguide
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DISCUSSION FORUMS
http://communities.sas.com
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DISCUSSION FORUMS
https://communities.sas.com/community/support-communities/text-analytics
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